Low-code Spark

Learn about the four main pillars of productive, powerful, low-code data engineering on Spark. See all the pieces in action!

In Part 1, we learned an easy way to troubleshoot a data pipeline using historical, read-only metadata. Now, I want to dig in and polish my individual spark data frames.

Let’s take advantage of Spark’s interim metadata to understand our Spark job behavior with low-code tooling.

Let's get started with PySpark using a visual interface to generate open-source Spark code.

Let's create a pipeline using Prophecy's visual, low-code interface for Spark.

Learn key steps to follow, how to define the ETL pipeline using Spark APIs and dataframes, and best practices for testing and optimizing pipelines for maximum efficiency