Databricks isn’t entirely open source. Here’s a breakdown of what is and isn’t:

Core Components (Open Source)

• Apache Spark: The core computational engine of Databricks is based on the open-source Apache Spark project.
• Delta Lake: An open-source storage layer that brings reliability and performance improvements to data lakes.
• MLflow: An open-source platform for managing the end-to-end machine learning lifecycle.

Proprietary Components (Not Open Source)

• Databricks Platform: The cloud-based platform itself, which includes features like:
  • Managed clusters
  • Collaborative notebooks
  • Integrated job scheduling
  • Security and governance tools
  • Unity Catalog (for data governance and lineage)
• Databricks Runtime: A curated and optimized version of Apache Spark with additional libraries and performance enhancements.

Why this model?

• Innovation: The proprietary components allow Databricks to provide features and optimizations not readily available in the core open-source projects.
• Ease of Use: The managed platform simplifies setup, deployment, and maintenance for users, especially in cloud environments.
• Commercial Sustainability: The commercial side supports the funding and development of the open-source projects Databricks contributes to.

In Summary:

Databricks leverages the power of open source while offering a value-added platform with enhanced features and a focus on user experience.