Post 1 Get started with Apache Spark in part 1 of our series, where we leverage Databricks and PySpark.
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Post 2 Easy DataFrame cleaning techniques, ranging from dropping problematic rows to selecting important columns.
Post 3 Using PySpark to apply transformations to real datasets.
Post 4 Continuing to apply transformations to Spark DataFrames using PySpark.
Post 5 Become familiar with building a structured stream in PySpark using the Databricks interface.
Post 6 Working with Spark's original data structure API: Resilient Distributed Datasets.
Post 7 Perform SQL-like joins and aggregations on your PySpark DataFrames.