Comparing Apache SparkTM and Databricks

Apache Spark capabilities provide speed, ease of use and breadth of use benefits and include APIs supporting a range of use cases:
- Data integration and ETL
- Interactive analytics
- Machine learning and advanced analytics
- Real-time data processing

Databricks builds on top of Spark and adds:
- Highly reliable and performant data pipelines
- Productive data science at scale
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Feature Comparison
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DATABRICKS RUNTIME
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Run multiple versions of Spark | ||
Built-in file system optimized for cloud storage access (AWS S3, Redshift, Azure Blob) | ||
Serverless pools offering auto-configuration of resources for SQL and Python workloads | ||
Spark-native fine grained resource sharing for optimum utilization | ||
Fault isolation of compute resources | ||
Faster writes to S3 | ||
Compute optimization during joins and filters | ||
Rapid release cycles | ||
Auto-scaling compute | ||
Auto-scaling local storage | ||
High availability for cluster | ||
Multi-user cluster sharing | ||
Automatic migration between spot and on-demand instances | ||
Second-level billing |
MANAGED DELTA LAKE
|
ACID transactions | ||
Schema management | ||
Batch/Stream read/write support | ||
Data versioning | ||
Performance optimizations |
INTEGRATED WORKSPACE
|
Interactive notebooks with support for multiple languages (SQL, Python, R and Scala) | ||
Real-time collaboration | ||
Notebook revision history and GitHub integration | ||
One-click visualizations | ||
Publish notebooks as interactive dashboards |
PRODUCTION JOBS AND WORKFLOWS
|
Spark job monitoring alerts | ||
One-click deployment from notebooks to Spark Jobs | ||
APIs to build workflows in notebooks | ||
Production streaming with monitoring |
ENTERPRISE SECURITY
|
Access control for notebooks, clusters, jobs, and structured data | ||
Audit logs | ||
SSO with SAML 2.0 support | ||
Data encryption (at rest and in motion) | ||
Compliance (HIPAA, SOC 2 Type 2) |
INTEGRATIONS
|
Connect other BI tools via authenticated ODBC/JDBC (Tableau, Looker, etc) | ||
REST API | ||
Data source connectors |
EXPERT SUPPORT
|
Help and support from the committers who engineer Spark | ||
SQL support |