Sriw World of Coding

13/09/2026

13/09/2026

Most candidates think they fail Data Engineering interviews because of complex coding rounds. But the truth?

Most stumble on this seemingly simple question:
​"Can you explain Azure Data Factory and how its core architecture works?"

​When interviewers ask this, they aren't looking for a Wikipedia definition. They want to know if you understand what makes enterprise pipelines tick behind the scenes.

​Here is how you nail it every single time:
ADF isn't just a basic ETL tool; it's a cloud-native orchestration powerhouse.

To explain it like a Senior Data Engineer, anchor your answer on the 5 core pillars:
1️⃣ Pipelines & Activities: The logical unit of work and the actions performed (Copy, Transform, Control Flow).
2️⃣ Datasets: The exact schema pointer to your data stores.
3️⃣ Linked Services: The secure connection strings bridging ADF to your systems.
4️⃣ Integration Runtime (IR): The compute backbone (Azure IR, Self-Hosted IR for on-prem networks, Azure-SSIS for legacy workloads).
5️⃣ Triggers: The event or schedule driving automated ex*****on.

​Mastering how these pieces connect will set you miles apart from candidates who only know how to click buttons on the UI.

​👉 Check out the link in bio for the complete video breakdown and the companion Medium guide!
​

12/09/2026

That cold sweat hits different when you realize a production Lakehouse pipeline crashed at 2:15 AM—right in the middle of writing 50 million records. 🥶💥

​Most junior engineers panic and do the unthinkable: they jump into ADLS storage and start manually deleting files, or blindly hit "Rerun" and end up duplicating half the enterprise data warehouse.

​Here is what separates a textbook Data Engineer from someone who has actually survived production fires:

​When an Azure Data Factory + Databricks/Delta Lake pipeline blows up mid-run, your first thought shouldn't be "how do I restart it?"
​It should be:

1️⃣ Atomicity check: Did Delta Lake's transaction log (_delta_log) isolate the partial write, or did downstream dashboards consume dirty data?
2️⃣ Audit the blast radius: Never touch raw storage blindly. Use Delta Time Travel to snapshot the last consistent state.
3️⃣ Idempotent recovery: If your pipeline isn’t built to safely run twice on the same data slice, fixing the bug will only double the mess.

​In enterprise interview rooms, interviewers don't care if you've never broken anything. They want to know: When production burns, what is your exact playbook to recover without data loss?

​Drop a comment below: Have you ever broken production, or are you still waiting for your turn? 😅👇

​Follow Sriw World of Coding for practical, enterprise-grade Azure Data Engineeri

​

11/09/2026

Most data engineers think setting up an event trigger in Azure Data Factory is a no-brainer.

​Until an interviewer asks: "When would you choose a Custom Event Trigger over a Storage Event Trigger?"
​…and the room suddenly goes dead silent.

​I’ve seen experienced candidates mix this up completely. They assume both just react to file drops. But under the hood? They solve two entirely different architecture challenges.

​👉 Storage Event Trigger:
Tightly bound directly to your Azure Blob or ADLS Gen2 storage accounts. Think Microsoft.Storage.BlobCreated or BlobDeleted. Perfect for lightweight, direct file-ingestion workflows.

​👉 Custom Event Trigger:
Decoupled and routed via Azure Event Grid Custom Topics. Your pipeline triggers not just from a file landing, but from business logic—an upstream webhook, a microservice payload, cross-subscription events, or an external third-party API signal.

​The moment you blur the line between basic file arrival and decoupled enterprise event streaming is the exact moment an interviewer spots theoretical knowledge vs. real production battle scars.

​Want to master these architecture nuances so you never get caught off-guard in technical rounds?

​Drop a comment below: Which trigger do you use more in your current production stack?
​

10/09/2026

The room went completely silent when the interviewer looked at me and asked:
​"𝙒𝙚 𝙝𝙖𝙫𝙚 10𝙏𝘽 𝙤𝙛 𝙖𝙘𝙩𝙞𝙫𝙚 𝙥𝙧𝙤𝙙𝙪𝙘𝙩𝙞𝙤𝙣 𝙙𝙖𝙩𝙖 𝙞𝙣 𝙤𝙪𝙧 𝙥𝙧𝙞𝙢𝙖𝙧𝙮 𝙩𝙧𝙖𝙣𝙨𝙖𝙘𝙩𝙞𝙤𝙣𝙖𝙡 𝙙𝙖𝙩𝙖𝙗𝙖𝙨𝙚. 𝙔𝙤𝙪 𝙣𝙚𝙚𝙙 𝙩𝙤 𝙢𝙞𝙜𝙧𝙖𝙩𝙚 𝙞𝙩 𝙩𝙤 𝙖 𝙣𝙚𝙬 𝙖𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚. 𝙕𝙚𝙧𝙤 𝙙𝙤𝙬𝙣𝙩𝙞𝙢𝙚. 𝙕𝙚𝙧𝙤 𝙙𝙧𝙤𝙥𝙥𝙚𝙙 𝙥𝙖𝙘𝙠𝙚𝙩𝙨. 𝙕𝙚𝙧𝙤 𝙘𝙪𝙨𝙩𝙤𝙢𝙚𝙧 𝙘𝙤𝙢𝙥𝙡𝙖𝙞𝙣𝙩𝙨. 𝙃𝙤𝙬 𝙙𝙤 𝙮𝙤𝙪 𝙢𝙤𝙫𝙚 𝙞𝙩 𝙬𝙞𝙩𝙝𝙤𝙪𝙩 𝙖𝙣𝙮𝙤𝙣𝙚 𝙣𝙤𝙩𝙞𝙘𝙞𝙣𝙜? "

​Most engineers instantly blurt out: "Just write a batch script or trigger a backup snapshot over the weekend."

​And that is exactly how you fail a Senior Data Engineering interview.
​Because running a naive SELECT * dump on a live 10TB OLTP database will lock tables, spike IOPS to 100%, kill API response times, and wake up your on-call team at 2 AM.

​Here is the battle-tested, production-safe playbook top data teams actually use:
1. ​Never Touch the Primary: Offload your initial snapshot reads strictly to a Read Replica or WAL stream to protect live customer traffic.
2. ​Chunked Historical Backfill: Slice the dataset into deterministic primary-key ranges with rate-limiting and dynamic backoff—never a monolithic dump.
3. ​Change Data Capture (CDC): Stream real-time mutations (inserts, updates, deletes) via tools like Debezium or Kafka while the cold backfill is underway.
4. ​Reconciliation & Hash Audits: Run dual-read shadow queries and row-level hash validations to ensure 100% data integrity before flipping a single switch.
5. ​Zero-Downtime Cutover: Drain the CDC buffer to near-zero lag, swap read traffic via feature flags, redirect writes, and keep an instant rollback switch hot.

​Moving terabytes of mission-critical data isn't just about speed—it's about invisible precision.

​If you want the complete architectural blueprint and the exact checklist to ace this interview question, read the full breakdown on Medium or head to my YouTube channel for in-depth system design deep dives!

​👉 Read the full Medium guide:

​

08/09/2026

Most engineers spend 3–4 months preparing for the AWS Solutions Architect Associate (SAA-C03) exam, and still walk into test day feeling unprepared.

I almost fell into the exact same trap.

When you open AWS documentation, it feels like an endless ocean of VPC subnets, IAM policies, and storage tiers. You watch hours of video tutorials, take endless notes, and the moment you attempt a practice test—you score 55%. That sinking feeling of "I just don't know enough" is real.

That’s when I realized: SAA-C03 isn't a test of memorization; it’s an architectural decision-making exam.

AWS doesn’t care if you know what an S3 bucket is. They want to know: Which storage class delivers cost-effective data retrieval within 5 minutes for audit archives?

Once I flipped my strategy from passive watching to scenario-based architecture mapping and root-cause practice test analysis, everything clicked. I cleared the SAA-C03 exam on my first attempt—and the blueprint works whether you have 6 months or 30 days.

I broke down my complete step-by-step roadmap, resources used, and the top high-yield topics in my detailed guide and companion video.

👉 Read the full step-by-step preparation guide on Medium: [Link in bio / First comment: https://medium.com//how-i-cracked-the-aws-certified-solutions-architect-associate-saa-c03-my-complete-preparation-a9283d71bbb6]

Follow me for more cloud architecture, certification blueprints, and real-world system design breakdowns! 🚀

08/09/2026