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