Discover our latest articles and guides on Data Engineering
ETL and ELT represent two approaches to data pipeline architecture. This comparison breaks down when to use each pattern, the tools that support them, and the interview questions data engineers face about pipeline design.
Master Great Expectations (GX) 1.22 for data quality validation. Covers Expectation Suites, Checkpoints, Data Docs, and common data engineering interview questions with practical Python examples.
Compare Apache Beam 2.76 and Spark 4.2 for data pipeline development. Covers portability, performance, real interview questions, and when to choose each framework.
Master Apache Flink 2.3 stream processing with event time semantics, watermarks, and windowing. Prepare for data engineering interviews with real-world examples.
Compare Apache Spark 4.2 and Databricks in 2026. Understand their architectures, performance characteristics, and master common interview questions for data engineering roles.
Compare Delta Lake and Apache Iceberg table formats for data lakehouse architecture. Covers ACID transactions, partition evolution, time travel, and common interview questions.