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What's the Big Idea? Building web apps with separate frontends, backends, and databases can be a headache. A monorepo puts everything in one place, making it easier to share code, develop locally, and test the whole app together. We showed how to build a simple signup dashboard using React, Node.js, PostgreSQL (with Prisma for easy database access), and optionally ClickHouse for fast data analysis, all within a monorepo structure. This setup helps you scale your app cleanly and makes life easier for your team. In this guide, we're going to build a super simple app that shows how many people sign up each day. We'll use: React: To make the pretty stuff you see on the screen.Node.js with Express: To handle the behind-the-scenes work and talk to the database.PostgreSQL: Our main place to store important info.Prisma: A clever tool that makes talking to PostgreSQL super easy and helps avoid mistakes.ClickHouse (optional): A really fast way to look at lots of data later on.All living together in one monorepo! Why Put Everything Together? Everything in one spot: Issues, updates, and reviews all happen in the same place.Easy peasy development: One setup for everything! Shared settings and simple ways to start the app.Sharing is caring (Code!): Easily reuse little bits of code across the whole app.Testing the whole thing: You can test how the frontend, backend, and database work together, all at once.Grows with you: Adding new parts to your app later is a breeze. What We're Making: A Signup Tracker Imagine a simple page that shows how many people signed up each day using a chart: Frontend (React): The part you see. It'll have a chart (using Chart.js) that gets the signup numbers and shows them.Backend (Express): The brain. It will grab the signup numbers from the database.Database (PostgreSQL): Where we keep the list of who signed up and when. We can also use ClickHouse later to look at this data in cool ways.Prisma: Our friendly helper that talks to PostgreSQL for us. How It's All Organized Plain Text visualization-monorepo/ │ ├── apps/ │ ├── frontend/ # The React stuff │ └── backend/ # The Node.js + Express + Prisma stuff │ ├── packages/ │ └── shared/ # Bits of code we use in both the frontend and backend │ ├── db/ │ └── schema.sql # Instructions for setting up our PostgreSQL database and adding some initial info │ ├── docker-compose.yml # Tells your computer how to run all the different parts together ├── .env # Where we keep secret info, like database passwords ├── package.json # Keeps track of all the tools our project needs └── README.md # A file explaining what this project is all about Setting Up the Database (PostgreSQL) Here's the basic structure for our db/schema.sql file: SQL CREATE TABLE users ( id SERIAL PRIMARY KEY, email TEXT NOT NULL, date DATE NOT NULL ); -- Let's add some fake signup data INSERT INTO users (email, date) VALUES ('[email protected]', '2024-04-01'), ('[email protected]', '2024-04-01'), ('[email protected]', '2024-04-02'); This code creates a table called users with columns for a unique ID, email address, and the date they signed up. We also put in a few example signups. Getting Prisma Ready (Backend) This is our apps/backend/prisma/schema.prisma file: SQL datasource db { provider = "postgresql" url = env("DATABASE_URL") } generator client { provider = "prisma-client-js" } model User { id Int @id @default(autoincrement()) email String date DateTime } This tells Prisma we're using PostgreSQL and where to find it. The User model describes what our users table looks like. Here's how we can use Prisma to get the signup counts: JavaScript const signups = await prisma.user.groupBy({ by: ['date'], _count: true, orderBy: { date: 'asc' }, }); This code asks Prisma to group the users by the date they signed up and count how many signed up on each day, ordering the results by date. Building the Backend (Express API) This is our apps/backend/src/routes/signups.ts file: TypeScript import express from 'express'; import { PrismaClient } from '@prisma/client'; const router = express.Router(); const prisma = new PrismaClient(); router.get('/api/signups', async (req, res) => { const data = await prisma.user.groupBy({ by: ['date'], _count: { id: true }, orderBy: { date: 'asc' }, }); res.json(data.map(d => ({ date: d.date.toISOString().split('T')[0], count: d._count.id, }))); }); export default router; This code sets up a simple web address (/api/signups) that, when you visit it, will use Prisma to get the signup data and send it back in a format the frontend can understand (date and count). Making the Frontend (React + Chart.js) This is our apps/frontend/src/App.tsx file: TypeScript import { useEffect, useState } from 'react'; import { Line } from 'react-chartjs-2'; function App() { const [chartData, setChartData] = useState([]); useEffect(() => { fetch('/api/signups') .then(res => res.json()) .then(setChartData); }, []); return ( <Line data={{ labels: chartData.map(d => d.date), datasets: [{ label: 'Signups', data: chartData.map(d => d.count) }], } /> ); } export default App; This React code fetches the signup data from our backend API when the app starts and then uses Chart.js to display it as a line chart. Sharing Code (Types) This is our packages/shared/types.ts file: TypeScript export interface SignupData { date: string; count: number; } We define a simple structure for our signup data. Now, both the frontend and backend can use this to make sure they're talking about the same thing: TypeScript import { SignupData } from '@shared/types'; Running Everything Together (Docker Compose) This is our docker-compose.yml file: YAML version: '3.8' services: db: image: postgres environment: POSTGRES_DB: appdb POSTGRES_USER: user POSTGRES_PASSWORD: pass volumes: - ./db:/docker-entrypoint-initdb.d backend: build: ./apps/backend depends_on: [db] environment: DATABASE_URL: postgres://user:pass@db:5432/appdb frontend: build: ./apps/frontend ports: - "3000:3000" depends_on: [backend] This file tells your computer how to run the PostgreSQL database, the backend, and the frontend all at the same time. depends_on makes sure things start in the right order. Super Fast Data Crunching (ClickHouse) If you have tons of data and want to analyze it really quickly, you can use ClickHouse alongside PostgreSQL. You can use tools to automatically copy data from PostgreSQL to ClickHouse. Why ClickHouse is Cool Blazing fast: It's designed to quickly count and group huge amounts of data.Great for history: Perfect for looking at trends over long periods.Plays well with others: You can use it with PostgreSQL as a separate place to do your analysis. Here's an example of how you might set up a table in ClickHouse: SQL CREATE TABLE signups_daily ( date Date, count UInt32 ) ENGINE = MergeTree() ORDER BY date; Making Development Easier Turborepo or Nx: Tools to speed up building and testing different parts of your monorepo.ESLint and Prettier: Keep your code looking consistent with shared rules.Husky + Lint-Staged: Automatically check your code for style issues before you commit it.tsconfig.base.json: Share TypeScript settings across your projects. Why This Is Awesome in Real Life One download: You only need to download the code once to get everything.One place for everything: Easier to manage updates and who owns different parts of the code.Fewer mistakes: Sharing code types helps catch errors early.Easy to get started: New team members can get up and running quickly. Wrapping Up Using a monorepo with React, Node.js, and PostgreSQL (with Prisma) is a smart way to build full-stack apps. It keeps things organized, makes development smoother, and sets you up for growth. Adding ClickHouse later on gives you powerful tools for understanding your data. Whether you're building a small project or something that will grow big, this approach can make your life a lot easier. What's Next? Add ways for users to log in securely (using tools like Clerk, Auth0, or Passport.js).Automatically copy data to ClickHouse for faster analysis.Add features like showing data in pages, saving data temporarily (caching with Redis), or letting users filter the data.Put your app online using services like Fly.io, Railway, Render, or Vercel (for the frontend).

In enterprise Kubernetes environments, particularly those supporting data science and analytics teams, managing namespace access becomes increasingly complex as user roles and responsibilities evolve. Teams often rely on centralized identity platforms like LDAP or Active Directory, where group entitlements define access rights. However, Kubernetes lacks native integration with LDAP, which forces teams to maintain RoleBindings manually — a tedious, error-prone, and unscalable process. This exact challenge emerged in our organization, where dozens of data scientists and engineers needed timely, accurate access to shared Kubernetes namespaces. We were stuck managing access through a manual process involving support tickets, group membership checks, and handcrafted YAML RoleBindings. It was slow, insecure, and operationally painful. To address this, I designed and implemented a Python-based automation system that synchronizes LDAP group entitlements with Kubernetes RoleBindings. It runs on a schedule via Kubernetes CronJobs, tracks recent LDAP changes, and ensures the cluster reflects the latest access policies. In this article, you’ll learn how this solution works, how to implement it, and the key lessons we learned from production use. The Problem: Manual Access Control Doesn’t Scale In many organizations, LDAP or Active Directory is the source of truth for user entitlements. Teams use group membership to define access to systems and data. However, Kubernetes has no built-in support for LDAP-based role-based access control (RBAC), which leads to several issues: When someone joins a project and is added to a group like ds-readonly, their Kubernetes access doesn't update automatically.Users who leave a project or change teams may retain access they no longer need.Access requests rely on manual RoleBinding creation, leading to slow onboarding and potential configuration errors. At our peak, we were processing 10–15 namespace access requests per week, each requiring validation and manual intervention. Revoking access was even worse — often overlooked until an audit or security review surfaced stale permissions. This not only slowed down developer productivity but also created serious compliance risks. Auditors found users with lingering edit access to sensitive namespaces months after they had left the corresponding LDAP group. Solution Overview: Automating With Python + CronJobs Here’s how the system works: LDAP Sync Script: Connects securely to the LDAP server, fetches group membership, and evaluates recent changes using whenChanged.Namespace Mapping via ConfigMap: LDAP entitlements are mapped to Kubernetes namespaces and access roles (e.g., view/edit) using a ConfigMap.RBAC Enforcement: Users are granted or revoked access via RoleBindings managed through kubectl.Scheduled CronJob: The automation runs every 4 hours inside a Kubernetes CronJob.Kubernetes Secrets: Securely store LDAP credentials and TLS CA certs. The result is a hands-free access lifecycle — when someone joins or leaves a group, their access updates automatically, without human intervention. Deep Dive: Python Script Logic 1. Connecting to LDAP Securely We use the ldap3 library and TLS to establish a secure connection. LDAP credentials and CA certificates are injected via Kubernetes Secrets to avoid hardcoding. Python from ldap3 import Server, Connection, Tls import ssl tls_config = Tls(validate=ssl.CERT_REQUIRED, version=ssl.PROTOCOL_TLSv1_2, ca_certs_file="/certs/ca.crt") server = Server("ldaps://ldap.example.com", use_ssl=True, tls=tls_config) conn = Connection(server, user=os.getenv("LDAP_USER"), password=os.getenv("LDAP_PASSWORD")) try: conn.bind() except Exception as e: logging.error(f"Failed to connect to LDAP: {e}") sys.exit(1) 2. Filtering Valid LDAP Users We extract human user IDs from group member strings using regex, skipping service accounts and irrelevant entries: Python import re pattern = r"CN=(c[f]\d{5,6})" for member in entry.member: match = re.search(pattern, str(member)) if match: entitlement_members.append(match.group().replace("CN=", "")) 3. ConfigMap-Based Namespace Mapping A Kubernetes ConfigMap named namespace-entitlement-mapping defines how entitlements map to namespaces and access types.: YAML data: ds-readonly: "data-science-ns,ro" ds-editor: "data-science-ns,rw" The script fetches this using: Python kubectl get configmap namespace-entitlement-mapping -o jsonpath={.data} Each entry tells the script which namespace to apply the RoleBinding in, and which ClusterRole (kubeflow-view or kubeflow-edit) to assign. 4. Managing RoleBindings Using kubectl, the script compares existing RoleBindings with current LDAP membership, and performs: Create RoleBinding if user is new. Update RoleBinding if access type changed.Delete RoleBinding if user is no longer in the group. Sample creation logic: Python kubectl apply -f /tmp/rolebinding-cf12345.yaml YAML template: Python apiVersion: rbac.authorization.k8s.io/v1 kind: RoleBinding metadata: name: namespace-cf12345 namespace: data-science-ns roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: kubeflow-view subjects: - kind: User name: cf12345 apiGroup: rbac.authorization.k8s.io This file is created in /tmp, applied with kubectl apply, and then removed. 5. Automation via Kubernetes CronJob The script runs every 4 hours via Kubernetes CronJob: YAML apiVersion: batch/v1 kind: CronJob metadata: name: ldap-rbac-sync spec: schedule: "0 */4 * * *" jobTemplate: spec: template: spec: containers: - name: ldap-sync image: registry.example.com/ldap-sync:latest env: - name: LDAP_USER valueFrom: secretKeyRef: name: ldap-creds key: username - name: LDAP_PASSWORD valueFrom: secretKeyRef: name: ldap-creds key: password volumeMounts: - name: ldap-cert mountPath: /certs volumes: - name: ldap-cert secret: secretName: ldap-ca restartPolicy: OnFailure This keeps our Kubernetes RBAC consistent with LDAP, without manual intervention. Results 90% drop in manual access requestsOnboarding time reduced from 2 business days to <4 hoursAccess stays accurate and auditable via logsIdempotent updates: Script only makes changes when needed Lessons Learned Avoid kubectl subprocesses for complex flows. They’re hard to test, parse, and secure. The Kubernetes Python client is a better long-term choice.LDAP’s whenChanged attribute is a huge performance win. Use it to avoid unnecessary syncing and reduce cluster churn.Always validate LDAP certificates. Skipping TLS validation introduces potential for man-in-the-middle attacks.Design for idempotency. Every change should be safe to repeat, especially in scheduled jobs.Start simple — a script + ConfigMap was all we needed. No need to introduce a complex policy engine at the outset. What’s Next? Although this solution works well for our current setup, future enhancements could include: This solution works well today, but future improvements could include:Switching to the Kubernetes Python API client for native RoleBinding managementEmitting Prometheus metrics for sync success/failureUsing annotations to track source entitlement for audit visibilitySupporting cloud identity platforms like Azure AD or Okta We also plan to extend this model to cluster-wide access controls, such as admin privileges for platform teams and temporary access windows for contractors. Conclusion This Python-based LDAP sync system helped us regain control over Kubernetes namespace access. It’s lightweight, secure, and designed for operational clarity. Best of all, it aligns access management with real-world team structures already defined in LDAP. If your team is still manually managing RBAC, this approach offers a practical path to automation — one that improves security, scales with your team, and reduces operational friction.

When disaster strikes, whether a natural disaster or a technical event, its impact on your network, database, and end-users can cause data corruption. Data corruption, whether sparked by hardware failures like dying disks or faulty RAM, software glitches such as operating system bugs, or human missteps like accidental overwrites, is a terrifying prospect for any administrator. Yet, it’s not a death sentence. Your PostgreSQL database is typically a dependable cornerstone of your operations. Still, when disaster strikes, it can swiftly morph into an inaccessible liability, bringing applications to a grinding halt and leaving critical data at risk. PostgreSQL 17 arms you with an enhanced arsenal to tackle this challenge head-on, offering built-in tools like pg_amcheck for pinpointing corruption, improved failover slot synchronization to keep replication intact during recovery, and finer-grained Write-Ahead Logging (WAL) control for precise restoration. In this blog, we'll dive deep into the disaster management process, equipping you with real-world commands and expected outputs to diagnose corruption accurately and recover effectively, whether you’re restoring from a robust backup or salvaging scraps from a crippled cluster with no safety net. With the right approach, you can turn panic into a plan and restore order to your database. Step 1: Detecting Corruption in PostgreSQL Corruption usually doesn’t introduce itself politely; it sneaks in through failed queries, panicked logs, or startup errors. Identifying corruption is the first step towards resolving it. Check the PostgreSQL Log Files Start by inspecting the log files. Typically, you'll find the log files in /var/log/postgresql/ or $PGDATA/pg_log. Within the log files, entry headers indicate the severity level of the log entry; for example: Shell ERROR: could not read block 128 in file "base/16384/12571": read only 0 of 8192 bytes PANIC: invalid page in block 42 of relation base/16384/16728 LOG: startup process (PID 1234) was terminated by signal 6: Aborted FATAL: the database system is in recovery mode Severity levels indicate: Shell ERROR: Read failure, possibly a disk-level issue. PANIC: Serious corruption PostgreSQL crashed to prevent further damage. FATAL: The server is trying to recover from an unclean shutdown. Best Practice: While excessive logging potentially wastes storage space, PostgreSQL has fine-grained logging control that allows you to customize the configuration parameters that specify logging events to provide the information you need to manage your system. We recommend you review the Postgres documentation and your settings to confirm that your settings are right for your system to use! pg_amcheck pg_amcheck is a powerful tool introduced as a core utility in PostgreSQL 17. It allows you to verify the physical integrity of heap tables and index structures without locking them, making them safe for live environments. It helps detect data corruption caused by storage failures or file-level inconsistencies by scanning data blocks and index pages for structural errors. To invoke pg_amcheck, use the command: Shell pg_amcheck -d mydb --all If your database is healthy, pg_amcheck returns: Shell No failures detected in database "mydb" If pg_amcheck detects corruption: Shell heap table "public.orders": block 128 is not valid btree index "public.orders_idx": block 22 contains invalid tuple Best Practice: Include the --heapallindexed or --rootdescend flags when you invoke pg_amcheck for deeper validation. Optional: Verify your checksums If you specified the --data-checksums flag when you initialized your PostgreSQL cluster (when running initdb), you can use the pg_checksums tool to detect low-level, file-based corruption across data blocks. pg_checksums provides an integrity safeguard, allowing PostgreSQL to verify whether the data on the disk has been altered unexpectedly due to bit rot, disk failures, or faulty hardware. pg_checksums must be run while the PostgreSQL server is stopped and will only work if checksums were enabled during cluster initialization. Running pg_checksums is especially important after unclean shutdowns, system crashes, or if you suspect disk issues. A clean report indicates that your data blocks are intact, while checksum failures identify specific block-level corruption in table or index files. SQL queries can map these file identifiers (like base/16384/12571) to table names. The tool doesn’t fix anything; it simply reports which blocks are damaged, allowing you to take the appropriate steps to recover (e.g., restore from backup, isolate affected tables, or investigate hardware issues). Always consider enabling checksums in production environments for better observability and earlier corruption detection. Shell sudo systemctl stop postgresql pg_checksums -c -D /var/lib/pgsql/data Checksum verification failed in file "base/16384/12571", block 128 Best Practice: Enable checksum verification when you initialize each new database. To enable pg_checksums on a new cluster, include the --data-checksums option when you invoke initdb: Shell initdb --data-checksums -D /var/lib/pgsql/data Step 2: Stop PostgreSQL Immediately When you find data corruption, you should prevent further damage by halting the service: Shell sudo systemctl stop postgresql postgresql.service - PostgreSQL RDBMS Active: inactive (dead) This will prevent PostgreSQL from continuing to write to WAL, potentially worsening data loss. Step 3: Restore from a Known Good Backup pgBackRest is a robust and efficient backup and restore solution for PostgreSQL that supports full, differential, and incremental backups, with compression, encryption, parallel processing, and offsite storage (for example, S3). pgBackRest is designed to handle large-scale environments with high performance and minimal impact on the database server. pgBackRest also simplifies disaster recovery by offering automated restore processes, archive management, and point-in-time recovery (PITR) capabilities. Clean and Restore the Cluster with pgBackRest Before you restore, take a backup of the corrupted (old) data directory: Shell cp -rf /var/lib/pgsql/data /var/lib/pgsql/data_backup After confirming that the backup is saved, wipe the old data directory: Shell rm -rf /var/lib/pgsql/data/* Then, restore from your last known good backup: Shell pgbackrest --stanza=main restore --db-path=/var/lib/pgsql/data INFO: restore command begin INFO: restored file base/16384/12571 (16MB, 50%) checksum verified INFO: restore command end: completed successfully Then, correct ownership: After restoring your database, ensure the data directory is correctly owned by the PostgreSQL user: Shell chown -R postgres:postgres /var/lib/pgsql/data Step 4: Use Point-in-Time Recovery (PITR) Using a backup strategy that supports Point-in-time recovery will allow you to stop right before corruption occurs. Configure Recovery Add the following commands to your postgresql.conf file: Shell restore_command = 'cp /mnt/backup/wal/%f %p' recovery_target_time = '2025-03-25 13:59:00 UTC' Create the recovery trigger: Shell touch /var/lib/pgsql/data/recovery.signal When you start PostgreSQL, you can watch the server recover to the point in time that you specified in the recovery_target_time parameter: Shell sudo systemctl start postgresql LOG: starting point-in-time recovery to "2025-03-25 13:59:00 UTC" LOG: restored log file "000000010000000000000005" from archive LOG: consistent recovery state reached LOG: recovery stopping before commit of transaction 123 LOG: database system is ready to accept connections Best Practice: Using a backup strategy that supports point-in-time recovery allows you to return to a clean state, just before corruption. Step 5: Salvage What You Can If you don’t have a backup but some tables still work, you can use pg_dump and other Postgres tools to extract what you can. First, use pg_dump to save the definitions of any readable tables and their data: Shell pg_dump -t customers mydb > customers.sql SQL SELECT count(*) FROM customers; Then, create a new cluster: Shell initdb -D /var/lib/pgsql/new_data --data-checksums pg_ctl -D /var/lib/pgsql/new_data -l logfile start Then, restore the salvaged data into your new cluster: Shell createdb -h /var/lib/pgsql/new_data newdb psql -d newdb < customers.sql Best Practice: Maintain a dependable backup strategy for any data that you can't afford to lose. In a crisis, you can use these steps to restore salvaged data, but the restoration may not be complete, and you will still need to manually review and recreate schema objects that may have been damaged. These steps will leave you with a partial recovery in a clean environment. Step 6: Use pg_resetwal as the Last Resort pg_resetwal is a low-level PostgreSQL utility used to forcibly reset a database cluster's write-ahead log (WAL), typically used as a last resort when the server cannot start due to missing or corrupted WAL files. This tool should be used cautiously, as it bypasses normal crash recovery and may lead to data inconsistency or loss of recent transactions. It is only safe to run when you are sure the data files are in a consistent state or when you're attempting to salvage partial data from a failed system. Only use this tool if all else fails. It resets WAL records, risking transaction loss and corruption. Shell pg_resetwal -f /var/lib/pgsql/data sudo systemctl start postgresql LOG: WAL reset performed LOG: database system is ready to accept connections Note: Data added since the last checkpoint may be lost; you should proceed only after consulting experts. Step 7: Prevent Future Corruption Don’t let this happen again. PostgreSQL 17 gives you excellent tools to stay protected. In summary, the best practices that can help you recover from a disaster are: Enable checksums when you initialize your cluster. Shell initdb --data-checksums -D /var/lib/pgsql/data Automate backups with pgBackRest. Shell pgbackrest --stanza=main --type=full backup pgbackrest --stanza=main --type=incr backup Run regular integrity checks with pg_amcheck. Shell pg_amcheck -d mydb --all > /var/log/pg_amcheck_$(date +%F).log Create a simple cron job to run pg_amcheck with the command: Shell 0 2 * * * pg_amcheck -d mydb --all > /var/log/pg_amcheck_$(date +\%F).log 2>&1 Step 8: Embrace High Availability and WAL Archiving If you have configured a replication solution that allows you to configure high availability and maintain backup nodes, you can promote a replica if the primary fails: Shell pg_ctl promote -D /var/lib/pgsql/standby Ensure that you have configured WAL Archiving for PITR; in your postgresql.conf file, set: Shell archive_mode = on archive_command = 'cp %p /mnt/wal_archive/%f' Conclusion Disaster recovery in PostgreSQL demands quick action and careful planning, and PostgreSQL 17 significantly strengthens your ability to respond. You can handle even the most critical failures with integrated tools like pg_amcheck for live corruption detection, pgBackRest for reliable backups and PITR, and pg_resetwal for last-resort recovery. Whether restoring from a clean backup, recovering to a point just before the disaster, or salvaging data from a damaged cluster, this post walks you through every step with actual commands and practical advice. Remember that recovery doesn’t start when something breaks. It begins with preparation. Make it a goal to turn your PostgreSQL database into a resilient, self-defending system by enabling data checksums, automating backups, monitoring for corruption, and setting up high availability with WAL archiving. In PostgreSQL, disaster may strike, but recovery can occur with the right tools and approach.

In computing, the execution of programs written in high-level languages requires that the source code be compiled to a low-level or native language. This compilation is referred to as Ahead-of-Time (AOT) and is typically done during build time. Effectively reducing the work to be done during runtime. In case of Java, the AOT produces an intermediate binary, viz. bytecode, which is then translated to native machine code during execution by the Java Virtual Machine (JVM). This is in line with Java’s philosophy of Write-Once-Run-Anywhere (WORA), or simply put, platform independence. During program execution, the JVM identifies frequently running code, referred to as hotspots, that could be optimized. This optimization is done by Just-In-Time (JIT) compiler during runtime. Fun fact: This is how HotSpot VM gets its name. JIT is used across programming languages such as .NET, JavaScript V8, and in some contexts within Python and PHP. In this article, we will focus on Just-In-Time (JIT) in Java only. JIT in JVM At runtime, the JVM loads the compiled code, viz., bytecode, and determines the semantics of each bytecode for appropriate computation. Bytecode interpretation at runtime requires computing resources, such as processor and memory, resulting in slower execution compared to a native application. JIT helps optimize Java program performance by compiling bytecodes to native code at runtime. The resulting natively compiled code is cached for later (re)use. During JVM startup, a large number of methods are called. If all these methods are compiled immediately, it would significantly affect the startup time. Thus, as a tradeoff between startup times and long-term performance, only those methods that are frequently called are compiled as soon as the JVM starts. Less-used methods are compiled later or not at all, depending on usage. To determine the threshold at which the method should be compiled is maintained internally by the JVM as a method invocation count. For every invocation, the counter is decremented. Once counter reaches zero, the JIT kicks in and compiles the method. Another counter maintained by JVM is for the loop back-edge. For every loop executed, the counter is checked against the threshold, beyond which it is too JIT-compiled for optimization. JIT Compilers JIT compliers come in below two flavors: C1: Client Compiler → C1 compiler has a low threshold for compilation and thus is optimized for quick application startup.C2: Server Compiler → C2 compiler has a higher threshold for compilation. Due to this, the profiling information available before compilation is much richer. Thus, the C2 compiled code is highly optimized for performance. Moreover, methods that are determined to be in the critical execution path of the application can be identified accurately by C2. Tiered Compilation JIT can compile the code at various optimization level depending upon its usage and complexity. For a higher level of optimization, although the program performs better, the compilation is costly in terms of resource utilization, viz. CPU and memory. To get the best of both C1 and C2, not only are they bundled together, but Tiered Compilation on multiple levels is done as described below. Levels of Tiered Compilation Level 0: Interpreted Code → During startup, all the bytecode is translated to native code. At this level, there is no optimization done. However, the frequently executed code is determined and profiled. This information is utilized in later levels for optimization.Level 1: Simple C1 Compiled Code → At this level, the low complexity methods are optimized, which the JVM considers as trivial. Neither profiling is done on these methods, nor are they optimized further.Level 2: Limited C1 Compiled Code → At this level, only a few of the hot methods are compiled with whatever profiling information is available. These methods are compiled for any early optimizations without waiting for C2. Note that these methods could later be (re)compiled at higher levels, viz. 3 or 4 additional profiles are captured.Level 3: Full C1 Compiled Code → At this level, all the non-trivial hot methods are compiled with full profiling information available. In most of the cases the JIT jumps directly from level 0 interpreted code to 3, unless compiler queues are full.Level 4: C2 Compiled Code → At this level JIT compiles the code with maximum optimization with all the available rich profiling information. For long-term execution, these compiled codes are most suitable. Since this is the peak of optimization, there is no further profiling information captured. It's interesting to note that a code could be (re)compiled multiple times for higher-level optimization, as deemed appropriate by the JIT. Deoptimization While JIT continuously strive to improve or optimize performance, there could be instances that the optimized methods are now irrelevant. Also, the compiler's assumptions about optimization could vary with the method’s behavior. For such instances, JIT temporarily reverts the optimization level back to previous or directly to level 0. Do note that these methods can again be optimized with newer profile information. However, it's advised to monitor such switching and recommended to adapt the source code accordingly to thwart the cost of frequent switching. Configurations JIT and Tiered compilation are enabled by default. They still could be disabled for strong reasons e.g. to diagnose any JIT induced errors (which is quite rare in nature) thus disabling should be avoided. To disable JIT either specify -Djava.compiler=NONE or -Xint as arguments during JVM startup. To disable tiered compilation, completely specify -XX:-TieredCompilation. For granular control, i.e., use only the C1 compiler, specify -XX:TieredStopAtLevel=1. To control the respective thresholds of various tiers from 2 to 4, refer below (use them by replacing Y with the number for the tier): -XX:TierYCompileThreshold=0-XX:TierYInvocationThreshold=100-XX:TierYMinInvocationThreshold=50-XX:TierYBackEdgeThreshold=1500 Do note that tweaking any of these configurations will have effects on the program's performance. Thus, it is advised to tweak only after thorough benchmarking. Conclusion JIT compilation enhances Java program performance by converting bytecode to native code at runtime, optimizing frequently used methods while balancing startup speed. Tiered compilation further refines this process by progressively optimizing code based on profiling data. While default JIT settings work well, fine-tuning configurations requires careful benchmarking to prevent performance drawbacks. For most applications, these optimizations happen seamlessly, without requiring developer intervention. However, understanding Just-in-Time (JIT) compilation is crucial for high-performance applications, where fine-tuning compilation settings can significantly impact execution efficiency. JDK Hotspot vs. GraalVM JIT GraalVM JIT is another implementation whose core differences with JDK HotSpot JIT is described below: JDK HotSpot → The standard HotSpot JVM uses a tiered JIT approach — featuring the simpler C1 compiler for quick optimizations and the more aggressive C2 (server) compiler for deeper optimizations. These compilers are predominantly written in C/C++ and have been honed over many years to offer stable and reliable performance for general Java workloads.GraalVM JIT → GraalVM builds on the HotSpot foundation by replacing the traditional C2 compiler with the Graal compiler. Written in Java, the Graal compiler introduces advanced optimizations such as improved inlining, partial escape analysis, and speculative optimizations. Additionally, GraalVM extends beyond just JIT improvements; it supports polyglot runtimes, enabling languages such as JavaScript and Python, and offers ahead-of-time (AOT) compilation to improve startup times and reduce memory overhead in suitable scenarios. In essence, while HotSpot remains a battle-tested and stable platform for running Java applications, GraalVM pushes the boundaries of performance and flexibility with its modern JIT compiler and additional runtime features. The choice between them usually depends on the specific workload and the performance or interoperability requirements of the application. References and Further Reading Just in Time CompilationTiered CompilationDebugging JIT Compiler

When it comes to auditing and monitoring database activity, Amazon Aurora's Database Activity Stream (DAS) provides a secure and near real-time stream of database activity. By default, DAS encrypts all data in transit using AWS Key Management Service (KMS) with a customer-managed key (CMK) and streams this encrypted data into a Serverless Streaming Data Service - Amazon Kinesis. While this is great for compliance and security, reading and interpreting the encrypted data stream requires additional effort — particularly if you're building custom analytics, alerting, or logging solutions. This article walks you through how to read the encrypted Aurora DAS records from Kinesis using the AWS Encryption SDK. Security and compliance are top priorities when working with sensitive data in the cloud — especially in regulated industries such as finance, healthcare, and government. Amazon Aurora's DAS is designed to help customers monitor database activity in real time, providing deep visibility into queries, connections, and data access patterns. However, this stream of data is encrypted in transit by default using a customer-managed AWS KMS (Key Management Service) key and routed through Amazon Kinesis Data Streams for consumption. While this encryption model enhances data security, it introduces a technical challenge: how do you access and process the encrypted DAS data? The payload cannot be directly interpreted, as it's wrapped in envelope encryption and protected by your KMS CMK. Understanding the Challenge Before discussing the solution, it's important to understand how Aurora DAS encryption works: Envelope Encryption Model: Aurora DAS uses envelope encryption, where the data is encrypted with a data key, and that data key is itself encrypted using your KMS key. Two Encrypted Components: Each record in the Kinesis stream contains: The database activity events encrypted with a data key The data key encrypted with your KMS CMK Kinesis Data Stream Format: The records follow this structure: JSON { "type": "DatabaseActivityMonitoringRecords", "version": "1.1", "databaseActivityEvents": "[encrypted audit records]", "key": "[encrypted data key]" } Solution Overview: AWS Encryption SDK Approach Aurora DAS encrypts data in multiple layers, and the AWS Encryption SDK helps you easily unwrap all that encryption so you can see what’s going on. Here's why this specific approach is required: Handles Envelope Encryption: The SDK is designed to work with the envelope encryption pattern used by Aurora DAS. Integrates with KMS: It seamlessly integrates with your KMS keys for the initial decryption of the data key. Manages Cryptographic Operations: The SDK handles the complex cryptographic operations required for secure decryption. The decryption process follows these key steps: First, decrypt the encrypted data key using your KMS CMK. Then, use that decrypted key to decrypt the database activity events.Finally, decompress the decrypted data to get the readable JSON output Implementation Step 1: Set Up Aurora With Database Activity Streams Before implementing the decryption solution, ensure you have: An Aurora PostgreSQL or MySQL cluster with sufficient permissions A customer-managed KMS key for encryption Database Activity Streams enabled on your Aurora cluster When you turn on DAS, AWS sets up a Kinesis stream called aws-rds-das-[cluster-resource-id] that receives the encrypted data. Step 2: Prepare the AWS Encryption SDK Environment For decrypting DAS events, your processing application (typically a Lambda function) needs the AWS Encryption SDK. This SDK is not included in standard AWS runtimes and must be added separately. Why this matters: The AWS Encryption SDK provides specialized cryptographic algorithms and protocols designed specifically for envelope encryption patterns used by AWS services like DAS. The most efficient approach is to create a Lambda Layer containing: aws_encryption_sdk: Required for the envelope decryption process boto3: Needed for AWS service interactions, particularly with KMS Step 3: Implement the Decryption Logic Here’s a Lambda function example that handles decrypting DAS events. Each part of the decryption process is thoroughly documented with comments in the code: Python import base64 import json import zlib import boto3 import aws_encryption_sdk from aws_encryption_sdk import CommitmentPolicy from aws_encryption_sdk.internal.crypto import WrappingKey from aws_encryption_sdk.key_providers.raw import RawMasterKeyProvider from aws_encryption_sdk.identifiers import WrappingAlgorithm, EncryptionKeyType # Configuration - update these values REGION_NAME = 'your-region' # Change to your region RESOURCE_ID = 'your cluster resource ID' # Change to your RDS resource ID # Initialize encryption client with appropriate commitment policy # This is required for proper operation with the AWS Encryption SDK enc_client = aws_encryption_sdk.EncryptionSDKClient(commitment_policy=CommitmentPolicy.FORBID_ENCRYPT_ALLOW_DECRYPT) # Custom key provider class for decryption # This class is necessary to use the raw data key from KMS with the Encryption SDK class MyRawMasterKeyProvider(RawMasterKeyProvider): provider_id = "BC" def __new__(cls, *args, **kwargs): obj = super(RawMasterKeyProvider, cls).__new__(cls) return obj def __init__(self, plain_key): RawMasterKeyProvider.__init__(self) # Configure the wrapping key with proper algorithm for DAS decryption self.wrapping_key = WrappingKey( wrapping_algorithm=WrappingAlgorithm.AES_256_GCM_IV12_TAG16_NO_PADDING, wrapping_key=plain_key, wrapping_key_type=EncryptionKeyType.SYMMETRIC ) def _get_raw_key(self, key_id): # Return the wrapping key when the Encryption SDK requests it return self.wrapping_key # First decryption step: use the data key to decrypt the payload def decrypt_payload(payload, data_key): # Create a key provider using our decrypted data key my_key_provider = MyRawMasterKeyProvider(data_key) my_key_provider.add_master_key("DataKey") # Decrypt the payload using the AWS Encryption SDK decrypted_plaintext, header = enc_client.decrypt( source=payload, materials_manager=aws_encryption_sdk.materials_managers.default.DefaultCryptoMaterialsManager( master_key_provider=my_key_provider) ) return decrypted_plaintext # Second step: decompress the decrypted data # DAS events are compressed before encryption to save bandwidth def decrypt_decompress(payload, key): decrypted = decrypt_payload(payload, key) # Use zlib with specific window bits for proper decompression return zlib.decompress(decrypted, zlib.MAX_WBITS + 16) # Main Lambda handler function that processes events from Kinesis def lambda_handler(event, context): session = boto3.session.Session() kms = session.client('kms', region_name=REGION_NAME) for record in event['Records']: # Step 1: Get the base64-encoded data from Kinesis payload = base64.b64decode(record['kinesis']['data']) record_data = json.loads(payload) # Step 2: Extract the two encrypted components payload_decoded = base64.b64decode(record_data['databaseActivityEvents']) data_key_decoded = base64.b64decode(record_data['key']) # Step 3: Decrypt the data key using KMS # This is the first level of decryption in the envelope model data_key_decrypt_result = kms.decrypt( CiphertextBlob=data_key_decoded, EncryptionContext={'aws:rds:dbc-id': RESOURCE_ID} ) decrypted_data_key = data_key_decrypt_result['Plaintext'] # Step 4: Use the decrypted data key to decrypt and decompress the events # This is the second level of decryption in the envelope model decrypted_event = decrypt_decompress(payload_decoded, decrypted_data_key) # Step 5: Process the decrypted event # At this point, decrypted_event contains the plaintext JSON of database activity print(decrypted_event) # Additional processing logic would go here # For example, you might: # - Parse the JSON and extract specific fields # - Store events in a database for analysis # - Trigger alerts based on suspicious activities return { 'statusCode': 200, 'body': json.dumps('Processing Complete') } Step 4: Error Handling and Performance Considerations As you implement this solution in production, keep these key factors in mind: Error Handling: KMS permissions: Ensure your Lambda function has the necessary KMS permissions so it can decrypt the data successfully.Encryption context: The context must match exactly (aws:rds:dbc-id) Resource ID: Make sure you're using the correct Aurora cluster resource ID—if it's off, the KMS decryption step will fail. Performance Considerations: Batch size: Configure appropriate Kinesis batch sizes for your Lambda Timeout settings: Decryption operations may require longer timeouts Memory allocation: Processing encrypted streams requires more memory Conclusion Aurora's Database Activity Streams provide powerful auditing capabilities, but the default encryption presents a technical challenge for utilizing this data. By leveraging the AWS Encryption SDK and understanding the envelope encryption model, you can successfully decrypt and process these encrypted streams. The key takeaways from this article are: Aurora DAS uses a two-layer envelope encryption model that requires specialized decryption The AWS Encryption SDK is essential for properly handling this encryption pattern The decryption process involves first decrypting the data key with KMS, then using that key to decrypt the actual events Proper implementation enables you to unlock valuable database activity data for security monitoring and compliance By following this approach, you can build robust solutions that leverage the security benefits of encrypted Database Activity Streams while still gaining access to the valuable insights they contain.

Doris's Tablet is damaged. Can it be repaired? Will data be lost? It's really hard to say. Why is it hard to say? This is mainly due to the following reasons: Apache Doris's data high-availability is based on multiple replicas. That is, when you create a table, if you specify three replicas, similar to the following parameters: Plain Text // specify 3 replicas "replication_allocation" = "tag.location.default: 3" //or "replication_num"="3" If one replica is damaged, users will hardly notice. Doris has an automatic repair function. However, if two replicas are damaged, the table can no longer be read from or written to, and manual repair is required. But these are all based on the high-availability scenario. What if there is only one replica? Doris has a default of three replicas. That is, if not specified during table creation, it is still three replicas. Only when the user specifically designates 1 replica will the above - mentioned situation occur (but sometimes, due to cost - effectiveness considerations or test scenarios, there are indeed single - replica situations). How to Judge Whether a Tablet Is Damaged? Generally, when the following error occurs during query: Plain Text Failed to get scan range, no queryable replica found in tablet: xxxx Or the following situation: Plain Text Failed to initialize storage reader,..., fail to Find path in version_graph Note: The reason for the following situation: The version may be lost during the replica migration process, which was fixed in 2.0.3. (It is recommended that users of old versions upgrade as soon as possible.) At this time, some tablets in the corresponding table are in an abnormal state, and need to be repaired according to the methods in the following sections. How to Repair a Damaged Tablet? When the above-mentioned situation occurs, the corresponding error message will carry a series of numbers of the tablet_id. Suppose the tablet_id is 606202, you can repair it in the following way. (When actually implementing, replace it with your own damaged tablet_id). Query Failure Situation 1, Show tablet xxxx (here, it's 606202) and get the detail cmd. 2. Execute the output of the detail cmd and find the replica where the BE is located (the compact status url contains the ip of the BE). 3. Execute curl <the compact status url in step 3>, in this example, it is curl http://be_ip:http_port/api/compaction/show?tablet_id=606202. Check the rowset and missing_rowset of this replica. Focus on the maximum version of the rowset (here it is 34) and missing_rowsets. From this, it can be seen that the rowset of this replica is 0 ~ 34, and there is no missing version in the middle (missing_rowsets is empty). Note: The special version here is actually the visible version of the partition. It can also be viewed through show partitions from <table - name xxx> where PartitionName = ''; The special version in the query statement is [0, 35], and this BE does not contain version 35. So version 35 needs to be added to this BE. If the missing version in the result of step 3 is not empty, for example, in the following: This indicates that some versions are indeed lost. If it is a three-replica scenario, check whether the other BEs are in the same situation. If they are all lost and the following information is in the logs of the corresponding BEs: It means that the three replicas are indeed damaged. This situation indicates that data is indeed lost. The safest way is to re-import data for the corresponding partition. If you really think that losing a little data doesn't matter for subsequent use, you can refer to the content in the following sections for repair. 4. First, confirm whether automatic repair is possible. If it is a multi-replica scenario, check whether there are healthy replicas. A healthy replica means version >= special version && last failed version = -1 && isBad = false, and when curling its compaction status, missing rowsets is empty. If there is such a replica, set the query - error - reporting replica as bad. Refer to the command: https://doris.apache.org/docs/sql-manual/sql-statements/table-and-view/data-and-status-management/SET-REPLICA-STATUS Wait for a while (it may take a minute or two), and then execute the detail cmd in step 2. If all replicas are healthy (version >= special version && last failed version = -1 && isBad = false), and when curling its compact status, missing rowsets is empty, it means the repair is successful. Execute "select count (*) from table" to check if it is OK. If there is no problem, the automatic repair is successful, and you don't need to read further. If there are still problems, continue reading. 5. Methods for filling empty rowsets If all three replicas are damaged or it is a single - replica situation, the method of filling empty rowsets can be used for repair. In this example, in the repair url, start_version = 35, end_version = 35; This example only lacks one rowset. In reality, there may be more missing (missing rowset, from the maximum version + 1 ~ special version). For however many rowsets are missing, call the repair method that many times; Refer to the command: https://doris.apache.org/docs/admin-manual/open-api/be-http/pad-rowset This kind of missing version can make the data queryable through the above - mentioned method, but this part of the data is lost, and there will be a situation of less data. 6. After repair, judge whether the last fail version needs to be modified. After the repair, execute "show tablet xxx" again. Check whether the last fail version of this replica is equal to -1. If its version is all filled, but last fail version = version + 1, the last fail version also needs to be manually changed to -1. Refer to the command: https://doris.apache.org/docs/sql-manual/sql-statements/table-and-view/data-and-status-management/SET-REPLICA-VERSION Lower - version Doris may not include this SQL. If this SQL is not supported and it is a single - replica or all multi - replicas are damaged, it cannot be recovered. If there is no problem, use "select count(*) from table_xx" to check whether it is readable. If it is readable, it is normal. Special Scenario Handling If it is a logging scenario—single-replica storage is used, but a certain tablet is damaged—losing some data is acceptable as long as it can be queried, and no separate repair is required. What should be done? Just set the variables skip_missing_version and skip_bad_tablet to true. The default is false. Summary Well, the above are the more common solutions. What if it still can't be fixed or you don't know how to do it? You need to take the initiative and find the Doris community members. They are all very enthusiastic! If you have repaired it through the above methods but still feel that it is unreasonable, why did the tablet damage occur? At this time, you can also bring the corresponding logs to the community members and let them assist in the analysis.

Scaling microservices for holiday peak traffic is crucial to prevent downtime and ensure a seamless user experience. This guide explores Azure DevOps automation, CI/CD pipelines, and cost-optimization strategies to handle high-demand traffic seamlessly. Manual scaling quickly becomes a bottleneck as organizations deploy dozens, sometimes hundreds, of microservices powered by distinct backend services like Cosmos DB, Event Hubs, App Configuration, and Traffic Manager. Multiple teams juggling these components risk costly delays and errors at the worst possible moments. This is where automation comes in: a game-changing solution that transforms complex, error-prone processes into streamlined, efficient operations. In this article, you’ll explore how automated pipelines can not only safeguard your systems during peak traffic but also optimize costs and boost overall performance in this Microservice world. The Challenge in a Microservices World Imagine a project with over 100 microservices, each maintained by different engineering teams. Every service may have its backend components, for example, as shown below: Cosmos DB: Used for storing data with low-latency access and high throughput.Event Hubs: Ingests telemetry and log data from distributed services.App Configuration: Centrally manages application settings and feature flags.Traffic Manager: Routes user traffic to healthy endpoints during failures. Manual Scaling Is Inefficient Coordinating these tasks manually is cumbersome,  especially when production issues arise. With multiple teams, interacting and collaborating on each microservice’s scaling and configuration can be overwhelming. This is where CI/CD pipelines and Infrastructure-as-Code (IaC) automation become crucial. Automation not only reduces human error but also provides a unified approach for rapid, reliable scaling and updates. Figure 1: A system overview showing how the Web App (Presentation Layer) interacts with microservices (Business Logic Layer), which use Cosmos DB, Event Hubs, and App Configuration (Data Layer). The Integration & Traffic Management layer, including Traffic Manager and Azure DevOps CI/CD, handles traffic routing, deployments, and Slack notifications. Understanding Each Component AKS (Azure Kubernetes Service) AKS is a managed Kubernetes service that simplifies deploying, scaling, and managing containerized applications. In a microservices environment, each service can be deployed as a container within AKS, with independent scaling rules and resource allocation. This flexibility enables you to adjust the number of pods based on real-time demand, ensuring that each service has the computing resources it needs. Cosmos DB Azure Cosmos DB is a globally distributed, multi-model NoSQL database service that delivers low latency and high throughput. In a microservices architecture, each service may have its own Cosmos DB instance to handle specific data workloads. Automation scripts can dynamically adjust throughput to meet changing demand, ensuring your service remains responsive even during peak loads. Event Hubs Azure Event Hubs is a high-throughput data streaming service designed to ingest millions of events per second. It’s particularly useful in microservices for collecting logs, telemetry, and real-time analytics data. By automating the scaling of Event Hubs, you ensure that your data ingestion pipeline never becomes a bottleneck,  even when the number of events spikes during high-traffic periods. App Configuration Azure App Configuration is a centralized service that stores configuration settings and feature flags for your applications. In a microservices ecosystem, different services often need unique settings or dynamic feature toggles. Instead of hard-coding these values or updating configurations manually, App Configuration provides a single source of truth that can be updated on the fly. During peak traffic, a microservice can instantly disable resource-heavy features without redeployment. Traffic Manager Azure Traffic Manager is a DNS-based load-balancing solution that directs user traffic based on endpoint health and performance. For microservices, it ensures that requests are automatically rerouted from failing or overloaded endpoints to healthy ones, minimizing downtime and ensuring a seamless user experience, especially during high-stress scenarios like holiday peak traffic. The Traffic Manager ensures disaster recovery by rerouting traffic from a failed region (e.g., East US) to a healthy backup (e.g., West US) in under 30 seconds, thereby minimizing downtime. Figure 2: High-level view of user traffic flowing through Azure Traffic Manager to an AKS cluster with containerized microservices, which interact with Cosmos DB, Event Hubs, and App Configuration for data, logging, and real-time updates. Automating the Process With CI/CD Pipelines Leveraging Azure DevOps CI/CD pipelines is the backbone of this automation. Here’s how each part fits into the overall process: Continuous integration (CI): Every code commit triggers a CI pipeline that builds and tests your application. This immediate feedback loop ensures that only validated changes move forward.Continuous delivery (CD): Once the CI pipeline produces an artifact, the release pipeline deploys it to production. This deployment stage automatically scales resources (like Cosmos DB and Event Hubs), updates configurations, and manages traffic routing. Dynamic variables, secure service connections, and agent configurations are all set up to interact seamlessly with AKS, Cosmos DB, and other services.Service connections and Slack notifications: Secure service connections (using a service account or App Registration) enable your pipeline to interact with AKS and other resources. Integration with Slack provides real-time notifications on pipeline runs, scaling updates, and configuration changes, keeping your teams informed. Figure 3: Component Diagram — A high-level architectural overview showing Azure DevOps, AKS, Cosmos DB, Event Hubs, App Configuration, Traffic Manager, and Slack interconnected. Core Automation Commands and Validation Below are the essential commands or code for each component, along with validation commands that confirm each update was successful. 1. Kubernetes Pod Autoscaling (HPA) Core Commands Shell # Update HPA settings: kubectl patch hpa <deploymentName> -n <namespace> - patch '{"spec": {"minReplicas": <min>, "maxReplicas": <max>}' # Validate update: kubectl get hpa <deploymentName> -n <namespace> -o=jsonpath='{.spec.minReplicas}{"-"}{.spec.maxReplicas}{"\n"}' #Expected Output: 3–10 Bash Script for AKS Autoscaling Here’s a shell script for the CI/CD pipeline. This is an example that can be adapted for other automation tasks using technologies such as Terraform, Python, Java, and others. Shell #!/bin/bash # File: scaling-pipeline-details.sh # Input file format: namespace:deploymentname:min:max echo "Logging all application HPA pod count before update" kubectl get hpa --all-namespaces -o=jsonpath='{range .items[*]}{.metadata.namespace}{":"}{.metadata.name}{":"}{.spec.minReplicas}{":"}{.spec.maxReplicas}{"\n"}{end}' cd $(System.DefaultWorkingDIrectory)$(working_dir) INPUT=$(inputfile) OLDIFS=$IFS IFS=':' [ ! -f $INPUT ] && { echo "$INPUT file not found"; exit 99; } while read namespace deploymentname min max do echo "Namespace: $namespace - Deployment: $deploymentname - min: $min - max: $max" cp $(template) "patch-template-hpa-sample-temp.json" sed -i "s/<<min>>/$min/g" "patch-template-hpa-sample-temp.json" sed -i "s/<<max>>/$max/g" "patch-template-hpa-sample-temp.json" echo "kubectl patch hpa $deploymentname --patch $(cat patch-template-hpa-sample-temp.json) -n $namespace" kubectl get hpa $deploymentname -n $namespace -o=jsonpath='{.metadata.namespace}{":"}{.metadata.name}{":"}{.spec.minReplicas}{":"}{.spec.maxReplicas}{"%0D%0A"}' >> /app/pipeline/log/hpa_before_update_$(datetime).properties #Main command to patch the scaling configuration kubectl patch hpa $deploymentname --patch "$(cat patch-template-hpa-sample-temp.json)" -n $namespace #Main command to validate the scaling configuration kubectl get hpa $deploymentname -n $namespace -o=jsonpath='{.metadata.namespace}{":"}{.metadata.name}{":"}{.spec.minReplicas}{":"}{.spec.maxReplicas}{"%0D%0A"}' >> /app/pipeline/log/hpa_after_update_$(datetime).properties rm -f "patch-template-hpa-sample-temp.json" "patch-template-hpa-sample-temp.json".bak done < $INPUT IFS=$OLDIFS tempVar=$(cat /app/pipeline/log/hpa_before_update_$(datetime).properties) curl -k --location --request GET "https://slack.com/api/chat.postMessage?token=$(slack_token)&channel=$(slack_channel)&text=------HPA+POD+Count+Before+update%3A------%0D%0ANamespace%3AHPA-Name%3AMinReplicas%3AMaxReplicas%0D%0A${tempVar}&username=<username>&icon_emoji=<emoji>" tempVar=$(cat /app/pipeline/log/hpa_after_update_$(datetime).properties) #below line is optional for slack notification. curl -k --location --request GET "https://slack.com/api/chat.postMessage?token=$(slack_token)&channel=$(slack_channel)&text=------HPA+POD+Count+After+update%3A------%0D%0ANamespace%3AHPA-Name%3AMinReplicas%3AMaxReplicas%0D%0A${tempVar}&username=<username>&icon_emoji=<emoji>" Create file: patch-template-hpa-sample.json JSON {"spec": { "maxReplicas": <<max>>,"minReplicas": <<min>>} 2. Cosmos DB Scaling Core Commands This can be enhanced further in the CI/CD pipeline with different technologies like a shell, Python, Java, etc. Shell # For SQL Database: az cosmosdb sql database throughput update -g <resourceGroup> -a <accountName> -n <databaseName> --max-throughput <newValue> # Validate update: az cosmosdb sql database throughput show -g <resourceGroup> -a <accountName> -n <databaseName> --query resource.autoscaleSettings.maxThroughput -o tsv #Expected Output: 4000 #Input file format: resourceGroup:accountName:databaseName:maxThroughput:dbType:containerName Terraform Code for Cosmos DB Scaling Shell # Terraform configuration for Cosmos DB account with autoscale settings. resource "azurerm_cosmosdb_account" "example" { name = "example-cosmosdb-account" location = azurerm_resource_group.example.location resource_group_name = azurerm_resource_group.example.name offer_type = "Standard" kind = "GlobalDocumentDB" enable_automatic_failover = true consistency_policy { consistency_level = "Session" } } resource "azurerm_cosmosdb_sql_database" "example" { name = "example-database" resource_group_name = azurerm_resource_group.example.name account_name = azurerm_cosmosdb_account.example.name } resource "azurerm_cosmosdb_sql_container" "example" { name = "example-container" resource_group_name = azurerm_resource_group.example.name account_name = azurerm_cosmosdb_account.example.name database_name = azurerm_cosmosdb_sql_database.example.name partition_key_path = "/partitionKey" autoscale_settings { max_throughput = 4000 } } 3. Event Hubs Scaling Core Commands This can be enhanced further in the CI/CD pipeline with different technologies like a shell, Python, Java, etc. Shell # Update capacity: az eventhubs namespace update -g <resourceGroup> -n <namespace> --capacity <newCapacity> --query sku.capacity -o tsv # Validate update: az eventhubs namespace show -g <resourceGroup> -n <namespace> --query sku.capacity -o tsv #Expected Output: 6 4. Dynamic App Configuration Updates Core Commands This can be enhanced further in the CI/CD pipeline with different technologies like a shell, Python, Java, etc. Shell # Export current configuration: az appconfig kv export -n <appconfig_name> --label <label> -d file --path backup.properties --format properties -y # Import new configuration: az appconfig kv import -n <appconfig_name> --label <label> -s file --path <input_file> --format properties -y # Validate update: az appconfig kv export -n <appconfig_name> --label <label> -d file --path afterupdate.properties --format properties -y #Input file format: Key-value pairs in standard properties format (e.g., key=value). 5. Traffic Management and Disaster Recovery (Traffic Switch) Core Commands This can be enhanced further in the CI/CD pipeline with different technologies like a shell, Python, Java, etc. Shell # Update endpoint status: az network traffic-manager endpoint update --endpoint-status <newStatus> --name <endpointName> --profile-name <profileName> --resource-group <resourceGroup> --type <type> --query endpointStatus -o tsv # Validate update: az network traffic-manager endpoint show --name <endpointName> --profile-name <profileName> --resource-group <resourceGroup> --type <type> --query endpointStatus -o tsv #Expected Output: Enabled #Input file format: profileName:resourceGroup:type:status:endPointName Terraform Code for Traffic Manager (Traffic Switch) JSON resource "azurerm_traffic_manager_profile" "example" { name = "example-tm-profile" resource_group_name = azurerm_resource_group.example.name location = azurerm_resource_group.example.location profile_status = "Enabled" traffic_routing_method = "Priority" dns_config { relative_name = "exampletm" ttl = 30 } monitor_config { protocol = "HTTP" port = 80 path = "/" } } resource "azurerm_traffic_manager_endpoint" "primary" { name = "primaryEndpoint" profile_name = azurerm_traffic_manager_profile.example.name resource_group_name = azurerm_resource_group.example.name type = "externalEndpoints" target = "primary.example.com" priority = 1 } resource "azurerm_traffic_manager_endpoint" "secondary" { name = "secondaryEndpoint" profile_name = azurerm_traffic_manager_profile.example.name resource_group_name = azurerm_resource_group.example.name type = "externalEndpoints" target = "secondary.example.com" priority = 2 } Explanation: These Terraform configurations enable autoscaling and efficient resource allocation for Cosmos DB and Traffic Manager. By leveraging IaC, you ensure consistency and optimize costs by provisioning resources dynamically based on demand. How to Reduce Azure Costs With Auto-Scaling Automation improves operational efficiency and plays a key role in cost optimization. In a microservices ecosystem with hundreds of services, even a small reduction in over-provisioned resources can lead to substantial savings over time. By dynamically scaling resources based on demand, you pay only for what you need. By dynamically adjusting resource usage, businesses can significantly reduce cloud costs. Here are concrete examples: Cosmos DB Autoscaling: For instance, if running 4000 RU/s costs $1,000 per month, reducing it to 1000 RU/s during off-peak hours could lower the bill to $400 monthly, leading to $7,200 in annual savings.AKS Autoscaler: Automatically removing unused nodes ensures you only pay for active compute resources, cutting infrastructure costs by 30%. Visualizing the Process: Sequence Diagram To further clarify the workflow, consider including a Sequence Diagram. This diagram outlines the step-by-step process, from code commit to scaling, configuration updates, and notifications, illustrating how automation interconnects these components. For example, the diagram shows: Developer: Commits code, triggering the CI pipeline.CI pipeline: Builds, tests, and publishes the artifact.CD pipeline: Deploys the artifact to AKS, adjusts Cosmos DB throughput, scales Event Hubs, updates App Configuration, and manages Traffic Manager endpoints.Slack: Sends real-time notifications on each step. Such a diagram visually reinforces the process and helps teams quickly understand the overall workflow. Figure 4: Sequence Diagram — A step-by-step flow illustrating the process from code commit through CI/CD pipelines to resource scaling and Slack notifications. Conclusion Automation is no longer a luxury — it’s the cornerstone of resilient and scalable cloud architectures. In this article, I demonstrated how Azure resources such as Cosmos DB, Event Hubs, App Configuration, Traffic Manager, and AKS can be orchestrated with automation using bash shell scripts, Terraform configurations, Azure CLI commands, and Azure DevOps CI/CD pipelines. These examples illustrate one powerful approach to automating microservices operations during peak traffic. While I showcased the Azure ecosystem, the underlying principles of automation are universal. Similar techniques can be applied to other cloud platforms. Whether you’re using AWS with CloudFormation and CodePipeline or Google Cloud with Deployment Manager and Cloud Build, you can design CI/CD workflows that meet your unique needs. Embrace automation to unlock your infrastructure’s full potential, ensuring your applications not only survive high-demand periods but also thrive under pressure. If you found this guide helpful, subscribe to my Medium blog for more insights on cloud automation. Comment below on your experience with scaling applications or share this with colleagues who might benefit! Your feedback is invaluable and helps shape future content, so let’s keep the conversation going. Happy scaling, and may your holiday traffic be ever in your favor! Further Reading and References Azure Kubernetes Service (AKS) Documentation: Guidance on deploying, managing, and scaling containerized applications using Kubernetes.Azure Cosmos DB Documentation: Dive deep into configuring and scaling your Cosmos DB instances.Azure Event Hubs Documentation: Explore high-throughput data streaming, event ingestion, and telemetry.Azure App Configuration Documentation: Best practices for managing application settings and feature flags in a centralized service.Azure Traffic Manager Documentation: Techniques for DNS-based load balancing and proactive endpoint monitoring.Terraform for Azure: Learn how to leverage Infrastructure as Code (IaC) with Terraform to automate resource provisioning and scaling.Azure DevOps Documentation: Understand CI/CD pipelines, automated deployments, and integrations with Azure services.

I keep finding myself in conversations with family and friends asking, “Is AI coming for our jobs?” Which roles are getting Thanos-snapped first? And will there still be space for junior individual contributors in organizations? And many more. With so many conflicting opinions, I felt overwhelmed and anxious, so I decided to take action instead of staying stuck in uncertainty. So, I began collecting historical data and relevant facts to gain a clearer understanding of the direction and impact of the current AI surge. So, Here’s What We Know Microsoft reports that over 30% of the code on GitHub Copilot is now AI-generated, highlighting a shift in how software is being developed. Major tech companies — including Google, Meta, Amazon, and Microsoft — have implemented widespread layoffs over the past 18–24 months. Current generative AI models, like GPT-4 and CodeWhisperer, can reliably write functional code, particularly for standard, well-defined tasks.Productivity gains: Occupations in which many tasks can be performed by AI are experiencing nearly five times higher growth in productivity than the sectors with the least AI adoption.AI systems still require a human “prompt” or input to initiate the thinking process. They do not ideate independently or possess genuine creativity — they follow patterns and statistical reasoning based on training data.Despite rapid progress, today’s AI is still far from achieving human-level general intelligence (AGI). It lacks contextual awareness, emotional understanding, and the ability to reason abstractly across domains without guidance or structured input.Job displacement and creation: The World Economic Forum's Future of Jobs Report 2025 reveals that 40% of employers expect to reduce their workforce where AI can automate tasks.And many more. There’s a lot of conflicting information out there, making it difficult to form a clear picture. With so many differing opinions, it's important to ground the discussion in facts. So, let’s break it down from a data engineer’s point of view — by examining the available data, identifying patterns, and drawing insights that can help us make sense of it all. Navigating the Noise Let’s start with the topic that’s on everyone’s mind — layoffs. It’s the most talked-about and often the most concerning aspect of the current tech landscape. Below is a trend analysis based on layoff data collected across the tech industry. Figure 1: Layoffs (in thousands) over time in tech industries Although the first AI research boom began in the 1980s, the current AI surge started in the late 2010s and gained significant momentum in late 2022 with the public release of OpenAI's ChatGPT. The COVID-19 pandemic further complicated the technological landscape. Initially, there was a hiring surge to meet the demands of a rapidly digitizing world. However, by 2023, the tech industry experienced significant layoffs, with over 200,000 jobs eliminated in the first quarter alone. This shift was attributed to factors such as economic downturns, reduced consumer demand, and the integration of AI technologies. Since then, as shown in Figure 1, layoffs have continued intermittently, driven by various factors including performance evaluations, budget constraints, and strategic restructuring. For instance, in 2025, companies like Microsoft announced plans to lay off up to 6,800 employees, accounting for less than 3% of its global workforce, as part of an initiative to streamline operations and reduce managerial layers. Between 2024 and early 2025, the tech industry experienced significant workforce reductions. In 2024 alone, approximately 150,000 tech employees were laid off across more than 525 companies, according to data from the US Bureau of Labor Statistics. The trend has continued into 2025, with over 22,000 layoffs reported so far this year, including a striking 16,084 job cuts in February alone, highlighting the ongoing volatility in the sector. It really makes me think — have all these layoffs contributed to the rise in the US unemployment rate? And has the number of job openings dropped too? I think it’s worth taking a closer look at these trends. Figure 2: Employment and unemployment counts in the US from JOLTS DB Figure 2 illustrates employment and unemployment trends across all industries in the United States. Interestingly, the data appear relatively stable over the past few years, which raises some important questions. If layoffs are increasing, where are those workers going? And what about recent graduates who are still struggling to land their first jobs? We’ve talked about the layoffs — now let’s explore where those affected are actually going. While this may not reflect every individual experience, here’s what the available online data reveals. After the Cuts Well, I wondered if the tech job openings have decreased as well? Figure 3: Job openings over the years in the US Even with all the news about layoffs, the tech job market isn’t exactly drying up. As of May 2025, there are still around 238,000 open tech positions across startups, unicorns, and big-name public companies. Just back in December 2024, more than 165,000 new tech roles were posted, bringing the total to over 434,000 active listings that month alone. And if we look at the bigger picture, the US Bureau of Labor Statistics expects an average of about 356,700 tech job openings each year from now through 2033. A lot of that is due to growth in the industry and the need to replace people leaving the workforce. So yes — while things are shifting, there’s still a strong demand for tech talent, especially for those keeping up with evolving skills. With so many open positions still out there, what’s causing the disconnect when it comes to actually finding a job? New Wardrobe for Tech Companies If those jobs are still out there, then it’s worth digging into the specific skills companies are actually hiring for. Recent data from LinkedIn reveals that job skill requirements have shifted by approximately 25% since 2015, and this pace of change is accelerating, with that number expected to double by 2027. In other words, companies are now looking for a broader and more updated set of skills than what may have worked for us over the past decade. Figure 4: Skill bucket The graph indicates that technical skills remain a top priority, with 59% of job postings emphasizing their importance. In contrast, soft skills appear to be a lower priority, mentioned in only 46% of listings, suggesting that companies are still placing greater value on technical expertise in their hiring criteria. Figure 5: AI skill requirement in the US Focusing specifically on the comparison between all tech jobs and those requiring AI skills, a clear trend emerges. As of 2025, around 19% to 25% of tech job postings now explicitly call for AI-related expertise — a noticeable jump from just a few years ago. This sharp rise reflects how deeply AI is becoming embedded across industries. In fact, nearly one in four new tech roles now list AI skills as a core requirement, more than doubling since 2022. Figure 6: Skill distribution in open jobs Python remains the most sought-after programming language in AI job postings, maintaining its top position from previous years. Additionally, skills in computer science, data analysis, and cloud platforms like Amazon Web Services have seen significant increases in demand. For instance, mentions of Amazon Web Services in job postings have surged by over 1,778% compared to data from 2012 to 2014 While the overall percentage of AI-specific job postings is still a small fraction of the total, the upward trend underscores the growing importance of AI proficiency in the modern workforce. Final Thought I recognize that this analysis is largely centered on the tech industry, and the impact of AI can look very different across other sectors. That said, I’d like to leave you with one final thought: technology will always evolve, and the real challenge is how quickly we can evolve with it before it starts to leave us behind. We’ve seen this play out before. In the early 2000s, when data volumes were manageable, we relied on database developers. But with the rise of IoT, the scale and complexity of data exploded, and we shifted toward data warehouse developers, skilled in tools like Hadoop and Spark. Fast forward to the 2010s and beyond, we’ve entered the era of AI and data engineers — those who can manage the scale, variety, and velocity of data that modern systems demand. We’ve adapted before — and we’ve done it well. But what makes this AI wave different is the pace. This time, we need to adapt faster than we ever have in the past.

The race to implement AI technologies has created a significant gap between intention and implementation, particularly in governance. According to recent data from the IAPP and Credo AI's 2025 report, while 77% of organizations are working on AI governance, only a fraction have mature frameworks in place. This disconnect between aspirational goals and practical governance has real consequences, as we've witnessed throughout 2024-2025 with high-profile failures and data breaches. I've spent the last decade working with organizations implementing AI solutions, and the pattern is distressingly familiar: enthusiasm for AI capabilities outpaces the willingness to establish robust guardrails. This article examines why good intentions are insufficient, how AI governance failures manifest in today's landscape, and offers a practical roadmap for governance frameworks that protect stakeholders while enabling innovation. Whether you're a CTO, AI engineer, or compliance officer, these insights will help bridge the critical gap between AI aspirations and responsible implementation. The Growing Gap Between AI Governance Intention and Implementation "We're taking AI governance seriously" — a claim I hear constantly from tech leaders. Yet the evidence suggests a troubling reality. A 2025 report from Zogby Analytics found that while 96% of organizations are already using AI for business operations, only 5% have implemented any AI governance framework. This staggering disconnect isn't just a statistical curiosity; it represents real organizational risk. Why does this gap persist? Fear of slowing innovation: Teams worry that governance will stifle creativity or delay launches. In reality, well-designed guardrails accelerate safe deployment and reduce costly rework.Unclear ownership: Governance often falls between IT, legal, and data science, resulting in inertia.Lack of practical models: Many organizations have high-level principles but struggle to translate them into day-to-day processes, especially across diverse AI systems. AI Governance Maturity Model The Cost of Governance Failure: Real-World Consequences The consequences of inadequate AI governance are no longer theoretical. Throughout 2024 to 2025, we've witnessed several high-profile failures that demonstrate how good intentions without robust governance frameworks can lead to significant harm. Paramount’s Privacy Lawsuit (2025) In early 2025, Paramount faced a $5 million class action lawsuit for allegedly sharing users’ viewing data with third parties without their consent. The root cause? Invisible data flows are not caught by any governance review, despite the company’s stated commitment to privacy. Change Healthcare Data Breach (2024) A breach at Change Healthcare exposed millions of patient records and halted payment systems nationwide. Investigations revealed a lack of oversight over third-party integrations and insufficient data access controls, failures that robust governance could have prevented. Biased Credit Scoring Algorithms (2024) A major credit scoring provider was found to have algorithms that systematically disadvantaged certain demographic groups. The company had invested heavily in AI but neglected to implement controls for fairness or bias mitigation. What these cases reveal is not a failure of technology, but a failure of governance. In each instance, organizations prioritized technological implementation over establishing robust governance frameworks. While technology moved quickly, governance lagged behind, creating vulnerabilities that eventually manifested as legal, financial, and ethical problems. AI Risk Assessment Heat Map Beyond Compliance: Why Regulatory Frameworks Aren't Enough The regulatory landscape for AI has evolved significantly in 2024 and 2025, with divergent approaches emerging globally. The EU AI Act officially became law in August 2024, with implementation staggered from early 2025 onwards. Its risk-based approach categorizes AI systems based on their potential harm, with high-risk applications facing stringent requirements for transparency, human oversight, and documentation. Meanwhile, in the United States, the regulatory landscape shifted dramatically with the change in administration. In January 2025, President Trump signed Executive Order 14179, "Removing Barriers to American Leadership in Artificial Intelligence," which eliminated key federal AI oversight policies from the previous administration. This deregulatory approach emphasizes industry-led innovation over government oversight. These contrasting approaches highlight a critical question: Is regulatory compliance sufficient for effective AI governance? My work with organizations across both jurisdictions suggests the answer is a resounding no. Compliance-only approaches suffer from several limitations: They establish minimum standards rather than optimal practicesThey often lag behind technological developmentsThey may not address organization-specific risks and use casesThey focus on avoiding penalties rather than creating value A more robust approach combines regulatory compliance with principles-based governance frameworks that can adapt to evolving technologies and use cases. Organizations that have embraced this dual approach demonstrate significant advantages in risk management, innovation speed, and stakeholder trust. Consider the case of a multinational financial institution with which I worked in early 2025. Despite operating in 17 jurisdictions with different AI regulations, they developed a unified governance framework based on core principles such as fairness, transparency, and accountability. This principles-based approach allowed them to maintain consistent standards across regions while adapting specific controls to local regulatory requirements. The result was more efficient compliance management and greater confidence in deploying AI solutions globally. Effective AI governance goes beyond ticking regulatory boxes; it establishes a foundation for responsible innovation that builds trust with customers, employees, and society. Building an Effective AI Governance Structure Establishing a robust AI governance structure requires more than creating another committee. It demands thoughtful design that balances oversight with operational effectiveness. In January 2025, the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) released ISO/IEC 42001, the first international standard specifically focused on AI management systems. This landmark standard provides a comprehensive framework for organizations to design, implement, and maintain effective AI governance. Based on this standard and my work with organizations implementing governance structures, here are the key components of effective AI governance: Executive Sponsorship and Leadership Governance starts at the top. According to McKinsey's "The State of AI 2025" report, companies with CEO led AI governance are significantly more likely to report positive financial returns from AI investments. Executive sponsorship sends a clear message that governance is a strategic priority, not a compliance afterthought. This leadership manifests in concrete ways: Allocating resources for governance activitiesRegularly reviewing key risk metrics and governance performanceModeling responsible decision making around AI deployment Cross-Functional Representation Effective AI governance requires diverse perspectives. A model governance committee structure includes: Legal and compliance experts to address regulatory requirementsEthics specialists to evaluate value alignment and societal impactSecurity professionals to assess and mitigate technical risksBusiness leaders should ensure governance aligns with strategic objectivesTechnical experts who understand model capabilities and limitations This cross-functional approach ensures governance decisions incorporate multiple viewpoints and expertise, leading to more robust outcomes. Maturity Models and Assessment Frameworks Rather than treating governance as a binary state (present or absent), leading organizations use maturity models to guide progressive development. A typical AI governance maturity model includes five stages: Initial/Ad-hoc: Reactive approach with minimal formal processesDeveloping: Basic governance processes established but inconsistently appliedDefined: Standardized processes with clear roles and responsibilitiesManaged: Quantitative measurement of governance effectivenessOptimized: Continuous improvement based on performance metrics By assessing current maturity and mapping a path to higher levels, organizations can implement governance in manageable phases rather than attempting a comprehensive overhaul all at once. Tailored to Organizational Context While frameworks and standards provide valuable structure, effective governance must be tailored to your organization's specific context, including: Industry-specific risks and requirementsOrganizational culture and decision-making processesAI maturity and use case portfolioResource constraints and competing priorities A mid-sized healthcare provider I advised developed a streamlined governance process, specifically focused on patient data protection and clinical decision support, for their two highest-risk AI applications. This targeted approach allowed them to implement robust governance within resource constraints while addressing their most critical concerns. Building effective governance isn't about creating bureaucracy; it's about establishing the right structures to enable responsible innovation. When designed thoughtfully, governance accelerates AI deployment by increasing confidence in outcomes and reducing the need for rework. Ethical Frameworks and Control Mechanisms Moving from abstract principles to practical implementation is where many AI governance efforts falter. The key is translating ethical frameworks into concrete control mechanisms that guide day-to-day decisions and operations. Operationalizing AI Ethics Leading organizations operationalize ethical principles through structured processes that impact the entire AI lifecycle. Key approaches include: Ethical impact assessments: These structured evaluations, similar to privacy impact assessments, help identify and address ethical concerns before deployment. They typically examine potential impacts on various stakeholders, with particular attention to vulnerable groups and edge cases.Value-sensitive design: This approach incorporates ethical considerations into the technology design process itself, rather than treating ethics as a separate compliance check. By considering values like fairness, accountability, and transparency from the outset, teams create more robust systems with fewer ethical blind spots.Ethics review boards: For high-risk AI applications, dedicated review boards provide expert evaluation of ethical implications. These boards often include external experts to incorporate diverse perspectives and challenge organizational assumptions. Human-in-the-Loop Requirements Human oversight remains critical for responsible AI deployment. Effective governance frameworks specify when and how humans should be involved in AI systems, particularly for consequential decisions. A practical human-in-the-loop framework considers: Decision impact: Higher-impact decisions require greater human involvementModel confidence: Lower confidence predictions trigger human reviewEdge cases: Unusual scenarios outside normal patterns receive human attentionFeedback mechanisms: Clear protocols for humans to correct or override AI decisions One financial services organization I worked with implemented a tiered approach to credit decisions. Their AI system autonomously approved applications with high confidence scores and clear approval indicators. Applications with moderate confidence or mixed indicators were routed to human reviewers with AI recommendations. Finally, unusual or high-risk applications received full human review with AI providing supporting analysis only. This approach balanced efficiency with appropriate human oversight. Continuous Monitoring and Feedback Static governance quickly becomes outdated as AI systems and their operating environment evolve. Effective governance includes mechanisms for ongoing monitoring and improvement: Performance dashboards that track key metrics like accuracy, fairness, and user feedbackAutomated alerts for unusual patterns or potential driftRegular reviews of model behavior and decision outcomesClear channels for stakeholder concerns or complaints These mechanisms ensure that governance remains responsive to changing circumstances and emerging risks. Accountability Structures Clear accountability is essential for effective governance. This includes: Defined roles and responsibilities for AI development, deployment, and monitoringDocumentation requirements that create an audit trail for decisionsIncident response protocols for addressing issues when they ariseConsequences for bypassing governance requirements Without accountability, even well-designed governance frameworks can devolve into performative compliance rather than substantive risk management. The organizations that excel at ethical AI implementation don't treat ethics as a separate concern from technical development. Instead, they integrate ethical considerations throughout the AI lifecycle, supported by concrete processes, tools, and accountability mechanisms. Practical Steps for Implementation: From Theory to Practice Transitioning from governance theory to effective implementation requires a pragmatic approach that acknowledges organizational realities. Here are practical steps for implementing AI governance based on successful patterns I've observed: Start Small and Focused Rather than attempting to implement comprehensive governance across all AI initiatives simultaneously, begin with a focused pilot program. Select a specific AI use case with moderate risk and strategic importance, high enough stakes to matter, but not so critical that failure would be catastrophic. This approach allows you to: Test governance processes in a controlled environmentDemonstrate value to skeptical stakeholdersRefine approaches before broader deploymentBuild internal expertise and champions For example, a retail organization I advised began with governance for their product recommendation AI, an important but not mission-critical system. This allowed them to address governance challenges before tackling more sensitive applications, such as fraud detection or employee performance evaluation. Build Cross-Functional Teams with Clear Roles Effective governance requires collaboration across disciplines, but without clear roles and responsibilities, cross-functional teams can become inefficient talking shops rather than decision-making bodies. Define specific roles such as: Governance chair: Oversees the governance process and facilitates decision-makingRisk owner: Accountable for identifying and assessing potential harmsCompliance liaison: Ensures alignment with regulatory requirementsTechnical reviewer: Evaluates technical implementation and controlsBusiness value advocate: Represents business objectives and user needs Clarify which decisions require consensus versus which can be made by individual role-holders. This balance prevents both analysis paralysis and unilateral decisions on important matters. Leverage Visual Frameworks and Tools Visual tools can dramatically improve governance implementation by making abstract concepts concrete and accessible. Key visual frameworks include: AI risk assessment heat maps: These visualizations plot potential AI risks based on likelihood and impact, with color-coding to indicate severity. They help prioritize governance attention on the most significant concerns.Governance maturity dashboards: Visual representations of governance maturity across different dimensions help organizations track progress and identify improvement areas.Advanced cloud tools: Platforms like Amazon Bedrock Guardrails, SageMaker Clarify, and FmEval support bias detection, safety checks, and explainability. Automated CI/CD pipelines and monitoring (e.g., CloudWatch) ensure governance is embedded in deployment. These visual tools not only improve understanding but also facilitate communication across technical and non-technical stakeholders, a critical success factor for governance implementation. Embrace Progressive Maturity Implement governance in stages, progressively increasing sophistication as your organization builds capability and comfort. A staged approach might look like: Foundation: Establish a basic inventory of AI systems and a risk assessment frameworkStandardization: Develop consistent governance processes and documentationIntegration: Embed governance into development workflows and decision processesMeasurement: Implement metrics to track governance effectivenessOptimization: Continuously improve based on performance data and feedback This progressive approach prevents the perfect from becoming the enemy of the good. Rather than postponing governance until a comprehensive system can be implemented (which rarely happens), you can begin realizing benefits immediately while building toward more sophisticated approaches. Practical Example: Financial Services Governance Implementation A mid-sized financial institution implemented AI governance using this progressive approach in early 2025. They began with a focused pilot for their customer churn prediction model-important enough to justify governance attention but not directly involved in lending decisions. Their implementation sequence: Created a simple governance committee with representatives from data science, compliance, customer experience, and information securityDeveloped a basic risk assessment template specifically for customer-facing AI systemsEstablished monthly reviews of model performance with attention to fairness metricsImplemented a customer feedback mechanism to identify potential issuesGradually expanded governance to additional AI use cases using lessons from the pilot Within six months, they had established governance processes covering 80% of their AI portfolio, with clear risk reduction and improved stakeholder confidence. By starting small and focusing on practical implementation rather than perfect design, they achieved meaningful progress where previous governance initiatives had stalled in the planning phase. The key lesson: Perfect governance implemented someday is far less valuable than good governance implemented today. Start where you are, use what you have, and build capability progressively. Conclusion The gap between AI governance intentions and real-world outcomes is more than a compliance issue, and it’s a business imperative. As recent failures show, the cost of insufficient governance can be measured in lawsuits, lost trust, and operational chaos. But the solution isn’t to slow down innovation; it’s to build governance frameworks that enable responsible, scalable deployment. Start small, build cross-functional teams, use visual and automated tools, and progress iteratively. The organizations that master both the “why” and the “how” of AI governance will not only avoid harm-they’ll lead the next wave of sustainable AI innovation. How is your organization bridging the gap between AI hype and responsible governance? Share your experiences or questions in the comments below.

Overview A web dashboard serves as the “front panel” for an embedded product — whether that product is a rack-mounted industrial controller, a bike-mounted GPS tracker, or a battery-powered soil-moisture sensor buried in a greenhouse bed. Because the dashboard is delivered over plain HTTP(S) and rendered in any modern browser, users do not have to download a native app, install drivers, or worry about operating-system compatibility; the interface is as portable as a URL. Typical tasks include: Toggling outputs (relays, MOSFETs, LEDs)Inspecting live data such as temperature, humidity, current draw, or RSSIAdjusting parameters like Wi-Fi credentials, alarm set-points, sampling ratesCollecting diagnostics like log files or memory statistics for field support staff Implementation Approaches Embed an HTTP server — Mongoose, lwIP-HTTPD, MicroPython’s uHTTPD, or a hand-rolled socket handler - inside the firmware. Then choose, or mix, the patterns below. Each technique sits at a distinct point on the scale of resource cost versus user-experience richness. 1. CGI (Common Gateway Interface) Classic CGI ties a URL such as /led.cgi to a firmware function that executes and returns HTML: C int cgi_led(struct http_request *r){ bool on = gpio_toggle(LED); http_printf(r,"<h1>LED %s</h1>", on ? "ON":"OFF"); return 200; } Pros: Footprint under 4 kB flash and a few hundred bytes of RAM.Cons: Every interaction forces a full page refresh, so UX feels clunky. Validation is manual. 2. RESTful API (Representational State Transfer) REST treats device features as resources: HTTP GET /api/led → read state POST /api/led {"on":1} → set state GET /api/adc/3 → read channel 3 The dashboard becomes a single-page app delivered from /index.html; JavaScript fetch() calls the API and patches the DOM. Pros: Clear separation of data and presentation; easy to reuse endpoints in a mobile app or cloud shim.Tips: Version your URIs (/v1/...) and compress payloads. 3. Server-Side Includes (SSI) SSI injects runtime values into static HTML. Tokens like the following are swapped out by a callback before the file is sent. HTML <h2>Temp: <!--#getTemp-->°C</h2> Pros: dead-simple way to inject a few dynamic numbers.Cons: limited once you need richer interactivity.When to use: Read-heavy dashboards that refresh every few seconds.Limits: Tokens only inject text; they cannot change style or handle clicks. 4. WebSockets WebSockets upgrade the HTTP connection to full duplex, letting both sides push frames whenever they like - ideal for streaming vibration data or ticker-style logs. Typical flow: the dashboard loads, JavaScript calls new WebSocket("wss://device-ip/ws"), firmware keeps the socket handle, and an ISR queues sensor frames. Pros: Latency < 10 ms, supports binary frames for efficiency.Cons: Each open socket eats buffers; TLS roughly doubles RAM cost. 5. MQTT Over WebSockets If the firmware already speaks MQTT, bridge the broker over wss://device-ip/mqtt. JavaScript clients such as Eclipse Paho can then subscribe and publish. Example topic map: Plain Text data/temperature → periodically published by device cmd/led → written by dashboard; MCU subscribes Pros: Reuses existing infrastructure; offline mode works because the broker is onboard.Cons: Extra protocol headers inflate packets; QoS handshakes add chatter. Comparing Footprints and Performance ApproachFlashRAMLatencyUX CGI 3 - 8 kB <1 kB >200 ms * SSI 4 - 10 kB ~1 kB 150 ms ** REST 8 - 25 kB 2 - 4 kB 80 ms *** WS 18 - 40 kB 4 - 8 kB <10 ms ***** MQTT/WS 25 - 60 kB 6 - 12 kB 15 ms **** Securing the Dashboard Transport security: Serve everything over TLS; many MCUs include hardware AES, keeping overhead modest.Authentication: Use a signed, time-limited token instead of basic auth.Validation: Treat query strings and JSON bodies as hostile until parsed and bounds-checked.Rate limiting: Guard against runaway polling that can starve the CPU. Many defences can be compiled out for development builds to save RAM, then re-enabled for production firmware. Blending Techniques Real products rarely stick to a single pattern. A popular recipe involves delivering the SPA shell, exposing low-frequency configuration through REST, streaming high-rate metrics over WebSockets, and maintaining an SSI “fallback” page for legacy browsers. This hybrid keeps memory use modest while giving power users a slick, low-latency UI. Selecting a Strategy A quick rule: If you need millisecond-level telemetry, pick WebSockets; if you want integration with phone apps or cloud dashboards, use REST; and if you are squeezed for flash, fall back to CGI or SSI. Transitioning later is painless because the same embedded server can host multiple schemes side by side. Conclusion Implementing a device dashboard using an embedded web server offers a scalable, browser-based control panel for embedded systems. Techniques such as CGI, SSI, REST APIs, and WebSockets cater to different device capabilities and complexity requirements. For modern applications, REST APIs strike a balance between simplicity and power, making them ideal for implementing features like an embedded web dashboard. By choosing the right implementation approach and utilizing a lightweight embedded web server, developers can craft efficient, interactive, and user-friendly dashboards to control and monitor embedded devices. For a hands-on walkthrough, see a step-by-step simple REST API implementation (LED Toggle Example). This walks through a minimal example of building an embedded web dashboard using a REST API served by an embedded web server. You can also try Mongoose Wizard to create your WebUI dashboard in minutes. It is a no-code visual tool that enables developers to effortlessly build a professionally looking device dashboard (WebUI) and REST API without writing any frontend code. Transforming the MCU board into a browser-accessible web dashboard for control, monitoring, and updates. Whether for prototyping or building production devices, integrating a web dashboard into firmware gives end users intuitive and powerful control.