This article describes how Apache Spark is related to Databricks and the Databricks Data Intelligence Platform.
Apache Spark is at the heart of the Databricks platform and is the technology powering compute clusters and SQL warehouses. Databricks is an optimized platform for Apache Spark, providing an efficient and simple platform for running Apache Spark workloads.
The Databricks company was founded by the original creators of Apache Spark. As an open source software project, Apache Spark has committers from many top companies, including Databricks.
Databricks continues to develop and release features to Apache Spark. The Databricks Runtime includes additional optimizations and proprietary features that build on and extend Apache Spark, including Photon, an optimized version of Apache Spark rewritten in C++.
When you deploy a compute cluster or SQL warehouse on Databricks, Apache Spark is configured and deployed to virtual machines. You don’t need to configure or initialize a Spark context or Spark session, as these are managed for you by Databricks.
Databricks supports a variety of workloads and includes open source libraries in the Databricks Runtime. Databricks SQL uses Apache Spark under the hood, but end users use standard SQL syntax to create and query database objects.
Databricks Runtime for Machine Learning is optimized for ML workloads, and many data scientists use primary open source libraries like TensorFlow and SciKit Learn while working on Databricks. You can use workflows to schedule arbitrary workloads against compute resources deployed and managed by Databricks.
The Databricks platform provides a secure, collaborative environment for developing and deploying enterprise solutions that scale with your business. Databricks employees include many of the world’s most knowledgeable Apache Spark maintainers and users. The company continuously develops and releases new optimizations to ensure users can access the fastest environment for running Apache Spark.