You’ve built your ingestion, you’ve processed your data with Spark, and it’s all sitting neatly in your S3 “Gold” bucket. Now what? You can’t ask every business analyst to learn PySpark just to see last month’s sales.
In Part 1 we set up a Dataproc cluster, got familiar with HDFS, and touched on what a data lake actually is. Now it is time to get into the real work: writing PySpark code, understanding RDDs, moving data between HDFS, GCS, and BigQuery, and learning how to actually submit Spark jobs to Dataproc.
Chapter 5 is where things get interesting if you come from a traditional database background. We are leaving the nice structured world of BigQuery and entering the territory of raw files, distributed storage, and the Hadoop ecosystem. Welcome to the data lake.
Chapter 1 starts with a confession most of us in the data world can relate to. Adi Wijaya says he used to think data was clean. Neatly organized, ready to go. Then he actually worked with data in real organizations and realized most of the effort goes into collecting, cleaning, and transforming it. Not the fun machine learning part. The plumbing part.
Chapter 1 gave us the “who” and “why” of data engineering. Now it is time for the “where.” Where does all that data actually live? How do organizations store, organize, and serve billions of rows of information so that someone on the business side can pull up a dashboard and make a decision before lunch?