Data Engineering: ETL & PySpark

The course suits analysts and backend developers moving into data engineering, final-year students, and freshers in India applying for data engineer roles at service companies and product firms. You should be comfortable with SQL and basic Python. The early topics on data flow and modelling come first, because later Spark and streaming lessons assume you already understand grain, keys and idempotent loads. Engineers who already run Spark jobs can use the performance and reliability sections to Candidates who have worked only on warehouse reporting will find the dimensional modelling and slowly changing dimension lessons the clearest route into engineering interviews.

Each lesson explains one mechanism, such as why a broadcast hash join avoids a shuffle, or how a merge upsert keeps a pipeline safe to rerun. Practice follows, one multiple-choice question at a time, and every answer comes with an explanation of why the other options fail. Lessons unlock in order, so the course is guided and self-paced, and you build from pipeline design Explanations tie each answer back to the pipeline decision it supports, so the reasoning carries over to designs you have not seen before.

Udemy and Coursera host long video courses on Spark and data pipelines, which suit learners who want a full guided build from start to finish. freeCodeCamp offers free project-based tutorials in Python, a useful companion for writing code. This course is MCQ-based recall of the decisions data engineers make and defend, such as choosing ETL or ELT, Parquet or Avro, or repartition versus coalesce, and of what each choice does to a running job.

For data engineering, recall matters more than memorising syntax. Answer each practice set before reading the explanation, then sketch the pipeline from memory: source, landing zone, transformation, load and checks. Return to the questions you missed after a few days. Run the PySpark snippets in a notebook when you can, so the explanations match real plan output. Strengthen the SQL base with SQL Interview Mastery and the language base with Python Fundamentals. Finish each section by describing the failure you would expect if the pipeline ran twice, since rerun safety appears in many interview rounds.