Best Practices for Migrating Spark ETL Workloads to Databricks
Introduction
Migrating Spark ETL workloads to Databricks unlocks faster performance, lower costs, and enhanced scalability. With built-in support for Delta Lake, automated cluster management, and an optimized Spark engine, Databricks simplifies and modernizes your data pipelines.
In this blog, I will cover key best practices for a smooth and efficient migration, ranging from workload assessment to performance tuning. Whether you're lifting and shifting, refactoring, or re-architecting your pipelines, these steps will ensure you unlock the full potential of Databricks.
Assessment and Planning for Databricks Migration
A successful migration to Databricks begins with a structured approach, including a comprehensive inventory of existing workloads, assessing compatibility, and setting clear migration objectives.
- Inventory Existing Workloads: Classify your workloads as batch or streaming and document key assets for migration, including jobs, workflows, parameters, resource utilization, execution frequency, and data consumers. A comprehensive inventory will give you a clear overview of the migration-ready components.
- Determine Compatibility: Identify any proprietary or environment-specific features that might not directly translate to Databricks. You must also identify and assess cloud providers and external service dependencies to ensure smooth integration during migration.
- Set Migration Objectives: Define clear goals and success criteria for the migration, including cost of ownership, governance, performance improvements, scalability, and architecture simplicity.
- Choose the Right Migration Strategy: The key to a successful Databricks migration is choosing the right strategy: Lift-and-Shift, Refactor, or Re-architect. Each option has its trade-offs regarding effort, flexibility, and long-term scalability. Lift-and-Shift is the quickest strategy, involving minimal changes to workloads. It lets you immediately leverage Databricks’ performance and cost benefits, delivering a fast ROI. While it may not be a fully optimized architecture, it accelerates value realization and sets the stage for future enhancements.
For more details, please check out our documentation here.
Security and Access Control
As your data scales on Databricks, robust security and governance become critical. Databricks’ unified tools help centralize data permissions, enabling decentralized, secure innovation across teams.
Unity Catalog is the foundation for unifying governance, providing fine-grained permissions across catalogs, schemas, tables, views, and columns. It also captures runtime data lineage, enables easy data discovery via the catalog explorer, and allows for monitoring through audit logs. Unity Catalog also helps protect sensitive data with built-in data masking and row-level security.
Identity federation simplifies centralized user and group management by integrating with identity providers like Azure AD or Okta. SCIM provisioning automates syncing users and groups, supporting seamless access management and Single Sign-On (SSO) for an enhanced user experience.
Adopting the Data Mesh architecture for federated governance and assigning data ownership to domain-specific teams accelerates decision-making, empowers teams to manage their data, and enhances agility and scalability.
Terraform templates will also help automate resource deployment and maintain scalability across the platform, ensuring infrastructure consistency and security.
Refactor and Optimize Code
​​Optimizing and refactoring your code is crucial for improving compatibility and performance on Databricks while setting up a solid foundation for long-term success.
Adapt Code for Databricks Compatibility
While Lift-and-Shift can speed up deployment, legacy code might impact performance and stability. I'd like you to take this opportunity to audit and streamline your pipelines, identifying components that might not integrate seamlessly with Databricks. Common challenges include custom JARs, Hive UDFs, and infrastructure-specific configurations that may need refactoring to align with Databricks’ cloud-native environment. Proactively addressing these issues helps reduce technical debt and ensures smoother, more stable migrations.
Refactor Data Processing Logic
Refactoring legacy data processing logic modernizes your pipelines and simplifies the migration. Remorph Transpile and BladeBridge tools can automate SQL code conversion, reducing manual effort. Leveraging Delta Lake enhances reliability and performance with features like ACID transactions and schema enforcement, ensuring compatibility and providing a scalable foundation for future growth.
Migrate Streaming Data to Databricks: Simplify and Optimize Your Pipelines
Migrating streaming data pipelines to Databricks can unlock faster insights, improved performance, and reduce operational costs. Whether you're moving away from legacy stream processors or consolidating multiple tools, Databricks offers a seamless migration path, providing key features designed for streamlined performance, scalability, and ease of management.
Delta Live Tables (DLT): Simplifying Streaming Data Pipelines
DLT simplifies building and managing streaming pipelines by allowing you to use SQL or Python in a declarative format. DLT automatically handles orchestration, retries, and dependencies so that you can focus on pipeline logic instead of operational challenges.
DLT supports batch and streaming data, streamlining the migration of streaming workloads and improving pipeline maintainability by reducing the need for multiple tools or custom solutions.
Key Features of DLT
- Declarative Streaming Pipelines: Simplifies pipeline creation by eliminating low-level details like scheduling, retries, and state management, allowing developers to focus on business logic.
- Batch and Continuous Modes: Supports batch and real-time streaming, offering flexibility for various data processing needs.
- Data Quality Checks: Integrated data quality checks to ensure accurate data processing.
- Schema Evolution: Adapting to data model changes reduces manual effort and ensures pipeline robustness.
- Multi-Catalog Publishing: Allows data publishing to multiple catalogs for flexible access and management.
Auto Loader: Scalable Ingestion with Minimal Overhead
Auto Loader simplifies incremental file ingestion from cloud storage (e.g., S3, ADLS). It automatically infers schemas, adapts to schema changes in real-time, and handles high-volume, low-latency workloads. Auto Loader is ideal for streaming migrations, ensuring efficient, seamless data pipeline management.
Structured Streaming: Real-Time Analytics at Scale
Structured Streaming, built on Apache Spark, provides flexibility and resilience for your streaming architecture. Combined with Delta Lake, it offers low-latency processing, exactly-once semantics, and ACID compliance, enabling scalable and reliable real-time data pipelines.
Lakeflow Connect: Streamlined Integration with External Sources
Lakeflow Connect provides fully managed connectors to easily ingest data from SaaS applications and databases into your lakehouse. Powered by Unity Catalog, serverless compute, and DLT, it ensures fast, scalable, and cost-effective incremental data ingestion, keeping your data fresh and ready for downstream use.
Optimizing Data Storage and Access in Databricks
As data volumes grow, optimizing how data is stored and accessed becomes critical for maintaining performance, reducing costs, and ensuring reliability. Databricks offers several advanced features to optimize Delta Lake and enhance performance at scale.
Key Optimizations:
- Optimize and File Compaction: Address small file issues with auto compaction and optimized writes, but still rely on OPTIMIZE for manual fine-tuning. In addition, Liquid Clustering and Z-Ordering can be used to adjust data organization based on predicted fields to improve performance further. Disk Caching and Photon Execution Engine: Delta Caching stores frequently accessed data in memory or SSDs to reduce query latency, and Photon Execution Engine runs workloads and queries more efficiently.
- Predictive optimization: Removes the need to manage maintenance operations for Unity Catalog manually managed tables on Databricks.
If further reference is needed, please check the Best practices for performance efficiency, tune-file size, and delta vacuum documentation.
Cluster Management and Optimization in Databricks
Optimizing cluster configurations is essential for balancing performance and cost in Databricks. A workload-aware approach is key, and you should experiment with different cluster types and settings that best fit your pipeline requirements.
Best Practices:
- Job Clusters: Use job clusters for ephemeral workloads to minimize idle costs.
- Cluster Types: Select memory-optimized VMs for cache-heavy tasks and compute-optimized VMs for high-throughput transformations.
- Auto-Scaling: Enable autoscaling to adjust resources based on workload demand automatically.
- Serverless: Go serverless to eliminate cluster management overhead for ad hoc queries and BI dashboards.
If further reference is needed, please check Cluster Configuration, Cost optimization for the data lakehouse, Comprehensive Guide to Optimize Databricks, Spark, and Delta Lake Workloads, and Connect to serverless compute.
Testing and Validation for ETL Workload Migration
Migrating ETL workloads isn’t just about moving code; it's about preserving data quality, performance, and functional accuracy. Automate tests to validate data accuracy and pipeline dependencies, and benchmark key metrics to track performance improvements.
Recommended approach is automating testing with tools like Remorph Reconcile, DataCompy, or SQLglot. These tools help reduce manual effort, boosting confidence in your migration's success.
It is also essential to adopt a phased rollout strategy, running the new and legacy systems side by side for validation and monitoring before entirely switching to Databricks. This approach ensures a low-risk migration with minimal disruption.
Workflow Management and Orchestration in Databricks
Shifting orchestration into Databricks Workflows can streamline ETL management, improve dependency handling, and enable centralized monitoring. Databricks natively supports Apache Airflow via the DatabricksRunNowOperator, allowing you to migrate orchestration without breaking your existing DAGs. Over time, consider moving workflows to Databricks’ native visual interface and YAML-based configuration for more flexibility and ease of use.
Monitoring and Optimization Post-Migration
After migration, maintaining an efficient Databricks environment is crucial. Built-in tools like Spark UI, Databricks Metrics UI, System tables, and Cluster Event Logs can help you gain insights into execution, resource usage, and cluster behavior.
As workloads stabilize, fine-tune performance by analyzing real-time data. Adjust resources by right-sizing clusters, refining autoscaling, and optimizing partitioning or caching strategies to improve query performance and reduce I/O.
Additional references are available on Create a Dashboard, Usage Dashboard, and System Tables Overview.
Conclusion
Migrating to Databricks requires careful planning, execution, and ongoing optimization. By following a structured approach, assessing workloads, optimizing code, and leveraging Databricks’ powerful features, you can ensure a seamless migration that improves performance, scalability, and cost-efficiency without interrupting ongoing business. With Databricks, your organization can scale data processing pipelines while reducing operational costs, setting you up for short-term success and long-term growth.
