How to Prepare for Data Engineering Interview Questions for Top Product-Based Companies?

✨ I have faced Spark-based scenarios interviews at Morgan Stanley, Meesho, Walmart Global Tech India, Apple, ZS, Deutsche Bank, JPMorgan Chase & Co., Jio, Barclays, and Amazon & 𝐁𝐢𝐠 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐁𝐚𝐬𝐞𝐝 𝐌𝐍𝐂 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬.

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Explain the concept of lineage in Spark and its significance.

How would you handle skewed data in Spark?

Discuss the advantages and limitations of Spark's DataFrame API compared to RDDs.

Describe the different caching levels in Spark and when you would use each.

Explain the purpose and benefits of Spark's broadcast variables.

How does Spark utilize memory management and storage in its execution model?

Describe the differences between narrow and wide transformations in Spark.

Discuss the use cases and benefits of Spark Streaming compared to batch processing.

Explain how Spark handles data serialization and why it is important.

Discuss the factors that impact the performance of Spark jobs and how you would optimize them.

📌 𝐌𝐞𝐝𝐢𝐮𝐦 𝐭𝐨 𝐇𝐚𝐫𝐝 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

✅ You have a large dataset that doesn't fit in memory. How would you optimize your Spark job to handle this situation?

✅ You need to perform a join operation on two large datasets. What strategies would you employ to optimize the join performance?

✅ You are working with skewed data partitions in Spark. How would you handle data skewness and ensure optimal processing?

✅ You have a Spark job that is taking longer than expected to complete. What steps would you take to identify and troubleshoot performance bottlenecks?

✅ You need to process nested JSON data in Spark efficiently. What approaches and techniques would you use to handle the nested structure?

✅ You have a Spark job that involves iterative processing. What optimizations would you consider to reduce the overall execution time?

✅ You want to efficiently process and aggregate time-series data in Spark. How would you design your Spark job to handle this requirement?

✅ You have a Spark job that requires a custom partitioning strategy. How would you implement and apply this custom partitioner in your job?

✅ You have a Spark cluster with limited resources. How would you allocate resources and configure the cluster for optimal performance?

✅ You want to implement an incremental data processing pipeline in Spark. How would you design and implement the pipeline to handle incremental updates efficiently?

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