Ankita Gulati

You skip Airflow → Uber rejects you
You ignore Kafka → Flipkart rejects you
You ghost Docker → every startup rejects you

One missing tool = one closed door.

80% of Data Engineers apply confidently — and get rejected on tools they “heard of but never used.”

So here are the best free resources to actually learn them:

1. 𝗦𝗤𝗟
  YouTube — Ankit Bansal, Sumit Mittal
  Practice — LeetCode (https://leetcode.com/), DataLemur 🐵 (Ace the SQL & Data Interview) (https://datalemur.com/), HackerRank (https://lnkd.in/gQhUxiRy)

2. 𝗣𝘆𝘁𝗵𝗼𝗻
  YouTube — Codebasics, CodeWithHarry, Zach Wilson
  Practice — LeetCode (https://leetcode.com/)

3. 𝗣𝘆𝗦𝗽𝗮𝗿𝗸
  YouTube — Darshil Parmar
  Practice — DataVidhya

4. 𝗔𝗶𝗿𝗳𝗹𝗼𝘄
  YouTube — Marc Lamberti
  Practice — Build DAGs, schedule pipelines, debug failures

5. 𝗱𝗯𝘁
  YouTube — Kahan Data Solutions, dbt Labs
  Practice — Build models, test pipelines, documentation

6. 𝗞𝗮𝗳𝗸𝗮
  YouTube — Confluent, Stéphane Maarek
  Practice — Producers/Consumers, real-time pipelines

7. 𝗗𝗼𝗰𝗸𝗲𝗿 & 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀
  YouTube — TechWorld with Nana, Kunal Kushwaha
  Practice — Containerize apps, deploy, scale

Now you have the list — but knowing a tool and surviving it in production are two different things.

That’s exactly why I built something different - 𝗪𝗵𝗲𝗻 𝗧𝗵𝗲𝗼𝗿𝘆 𝗘𝗻𝗱𝘀: 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀.

100 real production scenarios with answers:
• Kafka lag before peak traffic — what do you do?
• Airflow DAG failing at 2 AM — how do you debug?
• Pipeline breaking in prod — how do you fix fast?

No theory. Only real-world thinking.

👉 𝗚𝗲𝘁 𝗮𝗰𝗰𝗲𝘀𝘀 𝗵𝗲𝗿𝗲:
https://lnkd.in/gxBr_q8G
𝗨𝘀𝗲 𝗰𝗼𝗱𝗲: PRODUCTION100

Start learning what actually gets you hired.

#dataengineering #techcareers #codinginterview #bigdata

If I’m taking your Data Engineering Interview, these are the kind of real-world, scenario-driven questions you should be ready for:

1️⃣ Write an SQL query to find the second highest salary from an employee table.
2️⃣ How do you handle NULL values in SQL joins while ensuring data integrity?
3️⃣ Write an SQL query to calculate customer churn rate over the last 6 months.
4️⃣ Design a fact table for an e-commerce platform – what dimensions and measures would you include?
5️⃣ Explain the difference between star schema and snowflake schema and when to choose each.
6️⃣ Write a Python script to validate data quality and detect anomalies before loading.
7️⃣ In PySpark, how would you efficiently join two very large DataFrames to avoid skew?
8️⃣ Write PySpark code to find the top 3 customers by revenue per region.
9️⃣ You are processing real-time data from Event Hub into Delta tables. How would you implement this?
🔟 How do you implement schema evolution in Delta Lake without breaking existing jobs?
1️⃣1️⃣ How would you implement Slowly Changing Dimensions (SCD Type 2) in a data warehouse?
1️⃣2️⃣ Late-arriving data is detected in a batch pipeline – how do you ensure correctness of historical reporting?
1️⃣3️⃣ How do you design a pipeline that supports both batch and streaming workloads simultaneously?
1️⃣4️⃣ What is your approach to building incremental data loads in Azure Data Factory pipelines?
1️⃣5️⃣ Your PySpark job is failing due to skewed joins. Walk through your debugging and optimization steps.
1️⃣6️⃣ Explain the architecture of Azure Databricks integrated with Delta Lake in a production environment.
1️⃣7️⃣ How do you optimize query performance and concurrency in a Synapse dedicated SQL pool?
1️⃣8️⃣ Your data lake contains sensitive PII data. What best practices do you follow to secure data and manage secrets?
1️⃣9️⃣ How do you establish end-to-end data lineage and governance using Microsoft Purview?
2️⃣0️⃣ Design an end-to-end analytics pipeline that ingests from Event Hub, processes data in Databricks, stores it in Synapse, and powers dashboards in Power BI.

Notice – these aren’t “theory” questions. They’re practical, scenario-based, and the exact type of questions top companies ask.

That’s why we built the 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗦𝗮𝗳𝗮𝗿𝗻𝗮𝗺𝗮: 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 company specific questions for Amazon, Microsoft, PayPal, ThoughtWorks & many more.

𝗚𝗲𝘁 𝘆𝗼𝘂𝗿 𝗰𝗼𝗽𝘆 𝗵𝗲𝗿𝗲: https://lnkd.in/gZZUqbBp

#dataengineering #interviewprep #pyspark #topmate