𝗔𝗪𝗦 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀

 𝗔𝗪𝗦 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗲𝘃𝗲𝗿𝘆 𝗖𝗹𝗼𝘂𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿, 𝗗𝗲𝘃𝗢𝗽𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿, 𝗮𝗻𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗸𝗻𝗼𝘄:

🖥️ 𝗖𝗼𝗺𝗽𝘂𝘁𝗲
✅ EC2
✅ Lambda
✅ ECS
✅ EKS

💾 𝗦𝘁𝗼𝗿𝗮𝗴𝗲
✅ S3
✅ EBS
✅ EFS

🗄️ 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
✅ RDS
✅ DynamoDB
✅ Aurora

🌐 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴
✅ VPC
✅ Route 53
✅ CloudFront

🔐 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆
✅ IAM
✅ KMS
✅ Secrets Manager

📊 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴
✅ CloudWatch
✅ CloudTrail

🔄 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻
✅ SNS
✅ SQS

💡 𝗦𝗲𝗻𝗶𝗼𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗠𝗶𝗻𝗱𝘀𝗲𝘁
✔ Build for High Availability
✔ Design for Scalability
✔ Follow Least Privilege IAM
✔ Prefer Serverless where it makes sense
✔ Encrypt everything with KMS
✔ Monitor proactively with CloudWatch
✔ Audit every API call with CloudTrail
✔ Decouple applications using SNS & SQS
✔ Optimize performance and cost—not just functionality

Kafka concepts

  Kafka concepts every Data Engineer should know:

✅ What is Kafka?
✅ Producer & Consumer
✅ Topics
✅ Partitions
✅ Replication
✅ Consumer Groups
✅ Offsets
✅ Offset Commit
✅ Message Retention
✅ Delivery Semantics
✅ High Watermark
✅ Log Compaction
✅ Idempotent Producer
✅ Transactions
✅ Consumer Rebalancing
✅ ZooKeeper vs KRaft
✅ ACL (Access Control List)
✅ Kafka Connect
✅ Kafka Streams
✅ Topic Configuration
✅ Log Segments
✅ Mirror Maker
✅ Dead Letter Queue (DLQ)
✅ Idempotent Consumer
These are the concepts interviewers use to test whether you've worked with Kafka in real-world systems.
Here are a few questions you should be able to answer:
• Why do we need partitions?
• What happens if a broker crashes?
• How does Kafka prevent duplicate writes?
• What is the difference between offset and offset commit?
• How does log compaction differ from retention?
• When should you use DLQ?
• What triggers consumer rebalance?
• What is the High Watermark?
• How does Mirror Maker replicate data?
• What is the difference between Kafka Connect and Kafka Streams?
• How does Exactly-Once processing actually work?
Knowing the definitions is easy.
Understanding why these features exist is what separates beginners from experienced Data Engineers.
Bookmark this guide it covers the Kafka concepts you'll revisit throughout your Data Engineering journey.

𝐂𝐨𝐫𝐞 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐘𝐨𝐮 𝐌𝐮𝐬𝐭 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 of ML:

 𝟔 𝐂𝐨𝐫𝐞 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐘𝐨𝐮 𝐌𝐮𝐬𝐭 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 of ML:

📈 Linear Regression —
Your starting point. Simple, interpretable, and powerful for trend forecasting. Remember: always choose the simplest model (Occam's Razor applies!).

🌳 Decision Trees—
The most explainable ML model. Can handle both classification and regression. Always prune to avoid overfitting.

📍 k-Nearest Neighbor —
Lazy learning at its finest. Perfect for large datasets, but beware the *curse of dimensionality*.

⚔️ Support Vector Machine —
One of the best off-the-shelf algorithms. The kernel trick makes it surprisingly powerful for non-linear problems.

🧠 Neural Networks —
Biologically inspired and incredibly flexible. Mastering backpropagation is non-negotiable.

🔵 Clustering (k-Means) —
Unsupervised learning that groups data by similarity. No labels needed!

The Golden Rule across ALL algorithms?
➡️ Preprocessing is everything. Handle missing values, remove outliers, normalize your data.

Playwright automation using TypeScript

  I implemented PDF content validation in Playwright automation using TypeScript, with the support of AI coding assistants.

How it works:
1. Download the PDF file using Playwright's download event
2. Save the file to a local downloads folder
3. Verify that the file was downloaded successfully
4. Read the PDF file and extract its text content using the pdf-parse library
5. Validate that the expected text exists in the PDF using assertions

// Wait for download event before clicking const downloadPromise = documentObjectPage.page.waitForEvent('download'); // Click on download button await documentObjectPage.downloadButton.click(); // Download Promise const download await downloadPromise; // save file to known path const savePath = path.join(downloadsDir, download.suggestedFilename()); await download.saveAs (savePath); // Store path for use in other methods downloadedFilePath = savePath; // Extract text from the downloaded PDF expect(fs.existssync (downloadedFilePath)).toBeTruthy(); const dataBuffer = fs.readFileSync (downloadedFilePath); const parser = new PDFParse({ data: dataBuffer }); const pdfData = await parser.getText(); console.log('Extracted PDF text:', pdfData.text); // verify text content expect(pdfData.text).toContain (Dummy PDF'); await parser.destroy();

Building a Playwright API Automation Framework from Scratch

 Day 1 - Building a Playwright API Automation Framework from Scratch

I've been meaning to build a proper API test automation framework for a while now, and I finally started today. Sharing my progress here as I go - partly to document it, partly to keep myself accountable.

🤔 Why Playwright for API testing? Most people know Playwright for UI automation, but it has a solid built-in API request client as well. No need for separate tools like Rest Assured or Supertest - one framework handles both UI and API. That's what drew me to it.

🛠️ Tech stack I chose:
→ Playwright Test - test runner + HTTP client
→ TypeScript - type safety and better code structure
→ Allure - for rich HTML reporting
→ dotenv - for environment variable management

📂 Folder structure I'm following:
Playwright_API_Framework/
├── src/
│ ├── clients/
│ │ └── baseApiClient.ts ← Base HTTP client
│ └── services/
│ ├── userService.ts ← User API operations
│ └── authService.ts ← Login / Register operations
├── tests/ ← Test files (coming next)
├── test-data/ ← Payloads and schemas (coming next)
├── fixtures/ ← Playwright fixtures (coming next)
├── playwright.config.ts ← Framework config
├── tsconfig.json ← TypeScript config
└── .env ← Environment variables

✅ What I implemented today:
1️⃣ tsconfig.json — configured TypeScript with strict mode, path aliases like @services/* and @clients/* to avoid messy relative imports
2️⃣ playwright.config.ts — set up parallel execution with 4 workers, Allure reporter, multi-environment projects (dev/staging/prod) with environment-specific base URLs loaded from .env
3️⃣ baseApiClient.ts — created a base class that wraps Playwright's request context and exposes clean get(), post(), put(), patch(), delete() methods
4️⃣ userService.ts and authService.ts — these extend BaseApiClient and expose endpoint-specific methods like getUsers(), createUser(), login() etc. This is what I would call the API Object Model - same concept as Page Object Model but for APIs.