Post 1Get started with Apache Spark in part 1 of our series, where we leverage Databricks and PySpark.
Post 2Apply transformations to PySpark DataFrames such as creating new columns, filtering rows, or modifying string & number values.
Post 3Easy DataFrame cleaning techniques ranging from dropping rows to selecting important data.
Post 4Become familiar with building a structured stream in PySpark using the Databricks interface.
Post 5Working with Spark's original data structure API: Resilient Distributed Datasets.
Post 6Perform SQL-like joins and aggregations on your PySpark DataFrames.