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IBM Data Engineer interviews are no joke — they go deep into data architecture, AWS cloud pipelines, SQL performance tuning, and PySpark optimization. If you’re already experienced in AWS, Snowflake, or modern ETL frameworks, these 20 questions (and their detailed answers) will help you bridge your AWS expertise with IBM’s enterprise data expectations. Let’s dive […]

Below are 20 PySpark interview questions that appear in almost every data engineering interview — along with concise, practical answers you can use to demonstrate mastery. 1. Difference Between RDD, DataFrame, and Dataset 👉 In Python, we mostly use DataFrames. 2. Lazy Evaluation Spark doesn’t execute transformations immediately. It builds a logical DAG (Directed Acyclic […]

In the modern data ecosystem, automation is key to building reliable, efficient, and scalable data pipelines. As data volumes and frequency of updates increase, manually managing ETL (Extract, Transform, Load) jobs becomes inefficient. That’s where Snowflake Streams and Tasks come in — offering a simple yet powerful way to handle Change Data Capture (CDC) and […]

👉 Use staging tables and commit data only after full batch success. For Spark streaming, rely on checkpointing. Idempotent operations like MERGE ensure no duplicates. 👉 Idempotency means re-running the pipeline multiple times gives the same result. Important to avoid duplicates after failure/restart. Implemented using UPSERT/MERGE, unique keys, or partition overwrite. 👉 Track batch metadata […]