Geeky Codes | AI Engineering, Data Engineering & Machine Learning Tutorials
How User-Defined Functions work inside Spark—and why experienced PySpark engineers treat them carefully You have a DataFrame. You need to apply a custom transformation. So you write a Python function: Then you turn it into a UDF: It works. But there is an important question: Should you actually use a UDF? In PySpark, the answer … Read more
This text explains the differences between narrow and wide transformations in Apache Spark. Narrow transformations process independently without redistributing data, while wide transformations necessitate data movement and often introduce shuffle operations, affecting performance. Understanding these concepts aids in Spark’s optimization and execution strategies, influencing stages, tasks, and overall efficiency.
A practical guide to reducing cloud spend without sacrificing performance, reliability, or SLAs A Databricks ETL pipeline can be technically optimized and still be financially inefficient. You may reduce a Spark job from 90 minutes to 30 minutes—but if the optimized version uses a cluster that costs three times as much, your cloud bill may … Read more
In Part 1, we covered the fundamentals of Spark, PySpark, Databricks, DAGs, lazy evaluation, partitioning, data skew, salting, AQE, migration validation, and common PySpark coding questions. But experienced Data Engineer interviews usually go one level deeper. Interviewers want to know: Can you actually design, optimize, and troubleshoot a production data platform? In this article, we’ll cover … Read more