Data Engineering Insights

The definitive list of PySpark interview questions asked at top tech companies in 2026 — from basics to advanced optimisation and Delta Lake.

A complete guide to the most common PySpark interview patterns at Amazon, Meta, Google and Databricks — with real questions and solutions.

ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD and more. Master these and you will crush any SQL interview.

Real data engineering interview questions asked at Amazon, Meta, Google, Apple and Netflix — with detailed answers and preparation strategy.

Master the three most commonly tested PySpark topics in data engineering interviews: groupBy aggregations, join strategies and window functions.

Every SQL concept you need for a data engineering interview: window functions, CTEs, query optimisation, and tricky aggregation problems.

Both are powerful tools but serve very different purposes. Learn when to reach for PySpark and when Pandas is the better choice.

Step by step guide to building a production-ready ETL pipeline using Python, covering extraction, transformation and loading patterns.

The most important concepts, patterns and questions you need to know for data engineering technical interviews this year.

Partitioning is one of the most misunderstood concepts in Spark. Here is everything you need to know to write high-performance PySpark code.

From generators and decorators to pandas optimisation and ETL patterns — everything Python-specific you need for a data engineering interview.

The medallion architecture is the industry-standard way to organise data in a lakehouse. Here is how to design and implement it with Delta Lake.

The complete Databricks interview cheat sheet — Delta Lake, Unity Catalog, Photon, MLflow, DLT, Structured Streaming and optimisation questions asked at top companies in 2026.