Data Integration Platform

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.

Empowering business data users to quickly and easily build scalable data pipelines on the lakehouse

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

Learn how the Texas Rangers use Prophecy and Databricks Lakehouse as the foundation of their data mesh architecture to gain a competitive advantage with low-code data engineering.

Leverage Prophecy’s low-code approach and integration with dbt Core to build SQL data pipelines without compromising on engineering best practices

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