Today, many organisations find their legacy enterprise data warehouses (EDWs) struggling to keep pace. EDWs often prove costly, inflexible, and unable to support modern analytics and AI workloads. Migrating to the Databricks Data Intelligence Platform offers a transformative solution, providing a unified, scalable, and cost-effective environment for all your data needs. This article provides a detailed guide to planning and executing a successful data warehouse migration to Databricks.

Why Migrate to Databricks?

Traditional EDWs were designed for historical reporting and struggle with the demands of modern data analytics. They often cannot handle semi-structured, unstructured, or real-time data, making them unsuitable for data science, machine learning (ML), and AI-powered innovations. Furthermore, EDWs typically use proprietary formats, leading to vendor lock-in and high scaling costs.

The Databricks Data Intelligence Platform overcomes these limitations by providing a unified environment for data, analytics, and AI. Built on open standards, it offers a common governance approach and integrates seamlessly with cloud infrastructure. Migrating to Databricks allows organisations to:

• Reduce Costs: Adopt a lower cost curve with a serverless, elastic cloud-native data warehouse.
• Simplify the Data Platform: Consolidate all data, analytics, and AI use cases into a single platform.
• Scale Efficiently: Leverage serverless computing to scale on demand, optimising resource utilisation and reducing management overhead.
• Accelerate Innovation: Empower data scientists and engineers with collaborative, self-service tools and open-source technologies like MLflow and Apache Spark.

Pre-Migration Planning

Before initiating the migration, careful planning is crucial. This involves assessing your current data warehouse environment, defining migration goals, and selecting the appropriate migration strategy.

1. Assessment of Current Environment:
   • Data Inventory: Identify all data assets, including tables, views, and data pipelines.
   • Workload Analysis: Understand the types of queries and reports currently executed on the EDW.
   • Data Quality: Assess the quality of data and identify any cleaning or transformation requirements.
   • Security and Governance: Review existing security policies, access controls, and compliance requirements.

2. Define Migration Goals:
   • Cost Reduction: Set targets for reducing infrastructure and operational costs.
   • Performance Improvement: Define goals for improving query performance and data processing speed.
   • Scalability: Determine future data and user growth requirements.
   • Innovation: Identify opportunities to leverage Databricks for new analytics and AI use cases.

3. Choose a Migration Strategy:
   • Big Bang Migration: Migrate all data and workloads at once. This approach is risky but can be faster.
   • Phased Migration: Migrate data and workloads in stages. This reduces risk and allows for iterative improvements.
   • Parallel Migration: Run both the EDW and Databricks in parallel, gradually shifting workloads to Databricks. This provides a fallback option and allows for thorough testing.

Migration Process

The migration process involves several key steps: data extraction, transformation, loading, and validation.

1. Data Extraction:
   
   • Identify Data Sources: Determine the sources from which data needs to be extracted.
   • Choose Extraction Tools: Select appropriate tools for extracting data from the EDW. Databricks offers and supports different extraction and integration tools.
   • Extract Data: Extract data in a format suitable for loading into Databricks, and ready to be processed.
2. Data Transformation:
   
   • Cleanse and Transform Data: Apply necessary data cleaning and transformation rules to ensure data quality and consistency.
   • Use Delta Live Tables: Consider using Delta Live Tables for building reliable and maintainable ETL pipelines.
3. Data Loading:
   
   • Load Data into Databricks: Load the transformed data into Databricks using Apache Spark. Migrate Parquet data lakes to Delta Lake to leverage Delta Lake’s features.
4. Validation and Testing:
   
   • Validate Data: Ensure that the migrated data is accurate and complete.
   • Test Workloads: Run existing queries and reports on Databricks to verify performance and functionality.
   • User Acceptance Testing (UAT): Involve business users in testing the migrated environment.

Leveraging Databricks Components for Migration

Databricks offers several key components that facilitate the migration process:

• Apache Spark: Provides a powerful engine for data processing and transformation. Spark’s in-memory processing and optimized execution engine enable faster query execution and data processing, efficiently handling large-scale datasets and complex computations. Its distributed architecture allows for horizontal scalability by adding more nodes to the cluster, making it suitable for large-scale data processing and analytics.
• Delta Lake: Ensures data reliability and performance with ACID transactions, data versioning, and schema enforcement.
• Unity Catalog: Provides unified governance across all data assets, ensuring data security and compliance. It simplifies data governance, allowing data engineers, analysts, and data scientists to focus on data manipulation rather than accessibility.
• Lakehouse Federation: Allows you to query data in external systems without moving it, enabling a phased migration approach.
• Databricks Partner Connect: Provides access to a range of partner solutions for data integration, ETL, and migration.

Ensuring Data Governance with Unity Catalog

Data governance is a critical consideration during migration. Databricks Unity Catalog offers a unified solution for managing permissions, access control, and data lineage. Key features include:

• Fine-Grained Access Control: Implement granular security policies at the table, column, and row levels.
• Data Discovery: Enable users to find and access data they have permission to use.
• Automated Data Lineage: Track data flow and dependencies to ensure data quality and compliance.
• Delta Sharing: Securely share data with external organisations without compromising governance.

Before setting up your Unity Catalog, a few things must be prepared and understood. Unity Catalog requires a Cloud Storage Solution like Azure ADLS gen2 as its “default” location to store your data. Also, understand that the Unity Catalog treats every data asset and entity as an object, and each one of these objects requires that you assign permissions or links to other objects.

Post-Migration Optimisation

After migrating to Databricks, it’s essential to optimise the environment for performance and cost-effectiveness.

• Monitor Performance: Use Databricks monitoring tools to identify performance bottlenecks.
• Optimise Queries: Tune SQL queries for optimal performance.
• Implement Data Skipping: Leverage Delta Lake’s data skipping capabilities to accelerate query performance.
• Automate Data Compaction: Configure automatic data compaction to optimise data storage.

Conclusion

Migrating your data warehouse to the Databricks Data Intelligence Platform is a strategic investment that can unlock significant benefits in terms of cost, scalability, and innovation. By following a structured migration process, leveraging Databricks’ powerful components, and ensuring robust data governance, organisations can successfully transition to a modern data environment that supports their evolving business needs.

Our deep expertise in this area is further validated by our recent achievement of Databricks Elite Partnership status, a testament to our commitment to delivering innovative data solutions.

As a next step, consider engaging with Devoteam as we are an elite partner and can develop a detailed migration plan tailored to your specific environment and requirements.