Databricks Runtime 15.4 vs 16.4 vs 17.0 – Key Differences and Upgrade Benefits

Databricks Runtime 15.4 vs 16.4 vs 17.0 – Key Differences and Upgrade Benefits

Databricks Runtime is at the core of the Lakehouse platform, delivering the latest improvements in Apache Spark, Photon, Delta Lake, and machine learning libraries. Over the past few releases, Databricks has made significant strides in performance, governance, and AI capabilities. In this blog, we’ll compare Databricks Runtime 15.4 (LTS), 16.4, and 17.0, highlighting the key differences, upgrade considerations, and benefits.

Understanding Databricks Runtime Versions

A Databricks Runtime (DBR) version includes:

• Apache Spark version (engine powering distributed computing).
• Delta Lake enhancements for optimized data storage and querying.
• Photon engine updates for faster SQL performance.
• Language and ML library upgrades (Python, Scala, R, MLflow, PyTorch, etc.).
• Security patches and governance features (e.g., Unity Catalog support).
• LTS (Long-Term Support) Runtimes (e.g., 15.4 LTS):
  • Provide extended support (bug fixes, security patches).
  • Recommended for stable production workloads where compatibility is key.
• Regular Runtimes (e.g., 16.4, 17.0):
  • Introduce new features and performance improvements.
  • Ideal for innovation, testing, and leveraging cutting-edge capabilities.

What to Consider When Upgrading Runtime Versions

Before upgrading to a new Databricks Runtime, evaluate:

• Feature Requirements: Are you leveraging Delta 3.x features, Liquid Clustering, or Photon optimizations only available in 16.x+?
• Library Compatibility: Check compatibility of PySpark, MLflow, and Python versions (e.g., Python 3.9 vs 3.10/3.11).
• Performance Gains: Upgrading from 15.4 → 17.0 can reduce job runtimes due to improved AQE (Adaptive Query Execution) and Photon improvements.
• Governance Needs: Unity Catalog integration is more robust in 16.4 and 17.0.
• Cost Optimization: Photon and query planner improvements in 16.x+ reduce shuffle costs and cluster time.

Checklist When Planning an Upgrade

Here is a checklist to ensure smooth migration:

1. Review Release Notes: Check for breaking changes in APIs, libraries, or deprecated features.
2. Test in Lower Environments: Run test pipelines in development with the target runtime.
3. Validate Delta Tables: Ensure compatibility with new Delta Lake features (e.g., column mapping, CDC).
4. Benchmark Workloads: Compare runtime performance and cost savings (e.g., Photon queries).
5. Update Dependencies: Upgrade Python libraries, ML packages, or other third-party connectors as needed.
6. Enable Optional Features Gradually: Test features like Liquid Clustering or Photon before enabling cluster-wide.

Key Features of Databricks Runtime 15.4 vs 16.4 vs 17.0

Feature Comparison Table

Highlights by Runtime:

Databricks Runtime 15.4 (LTS):

• Stable, production-ready with Spark 3.4.
• Limited Photon and Delta Lake 2.x improvements.
• Best for long-term workloads where stability is critical.

Databricks Runtime 16.4:

• Introduced Delta 3.x with Change Data Capture (CDC) and Delta Universal Format (DUF).
• Upgraded Photon v2, improving SQL query performance.
• Python 3.10 for modern library compatibility.
• Better Unity Catalog governance and fine-grained lineage.

Databricks Runtime 17.0:

• Latest Delta 3.x features + Liquid Clustering for flexible data layout.
• Advanced Photon optimizations and query planner improvements.
• Python 3.11 with faster ML and GenAI integrations.
• Unity Catalog audit logging improvements and enhanced BI integrations.
• Best for teams looking to leverage AI/ML and advanced Lakehouse features.

When to Choose LTS (15.4) vs Latest (17.0)

• Choose 15.4 LTS if:
  • You run mission-critical pipelines where stability is more important than cutting-edge features.
  • You want long-term support and fewer version upgrade cycles.
• Choose 17.0 if:
  • You need advanced performance optimizations (Photon v2).
  • Your use case involves Delta 3.x, CDC, Liquid Clustering, or AI/ML with Python 3.11.
  • You aim to future-proof your Lakehouse environment.

Conclusion

Upgrading from Databricks Runtime 15.4 (LTS) to 16.4 or 17.0 provides significant benefits in terms of performance, Delta Lake capabilities, governance, and AI/ML support. While 15.4 is a safe choice for stable production workloads, 17.0 offers cutting-edge features like Liquid Clustering, advanced Photon optimizations, and faster Python environments—making it the ideal choice for modern Lakehouse implementations and cost-efficient analytics.

References:

• https://learn.microsoft.com/en-us/azure/databricks/release-notes/runtime/16.4lts
• https://learn.microsoft.com/en-us/azure/databricks/release-notes/runtime/15.4lts
• https://learn.microsoft.com/en-us/azure/databricks/release-notes/runtime/17.0

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