Snowflake to Databricks Migration Guide - A Guide for Engineering Leaders
Today, companies assess their data platforms due to the growing complexity of analytics, AI, and machine learning. Switching from Snowflake to Databricks can allow a company to integrate its data engineering, analytics, and AI on one platform.
Snowflake to Databricks data migration is not limited to transferring tables and files from one data platform to another. Prior to performing any migrations of your workloads, you need to assess the data architecture, workload, pipeline, security, performance, and business requirements.
This Snowflake to Databricks migration guide mentions migration phases and their technical aspects.
Why Migrate from Snowflake to Databricks
Snowflake and Databricks offer distinct but similar functionality in modern data platforms. While Snowflake has always been a cloud data warehouse, Databricks is a data and AI platform powered by lakehouse architecture. The reasons for migration could be -
- Consolidate data engineering, analytics, and AI workloads
- Build data pipelines and analytics workflows on a unified platform
- Work directly with data stored in cloud object storage
- Support large-scale machine learning and AI workloads
- Reduce duplication between data lakes and data warehouses
- Adopt an open data architecture using technologies such as Delta Lake
- Simplify data processing and analytics infrastructure
The decision should not only depend on the capabilities of the platform. Engineers need to figure out how the current workloads, costs, requirements, experience, and future plans of the data architecture stack up.
Snowflake to Databricks Data Migration: Key Points to Consider
A well-defined approach enables organizations to ensure successful migration of their data. Organizations can study the environment, set priorities, and migrate data.
1. Assess Current Snowflake Environment
The first task involves conducting an inventory of the current Snowflake environment.
You need to assess:
- Databases and schemas
- Tables and views
- Stored procedures and functions
- Data volumes and growth rates
- ETL and ELT pipelines
- BI and reporting dependencies
- Data integrations
- User roles and access controls
- Scheduled workloads
- Queries and workload patterns
It will help determine which workloads should be migrated, modernized, consolidated, or eliminated.
2. Define the Target Databricks Architecture
The next step is to define how the current Snowflake workloads will map onto Databricks
Teams need to define:
- Databricks workspace architecture
- Catalog and schema structure
- Storage architecture
- Delta Lake tables
- Compute requirements
- Data ingestion patterns
- Security and governance controls
- BI and analytics integrations
The organization should not rebuild the Snowflake architecture within Databricks. It is an opportunity for them to redesign its architecture for the target platform.
3. Development of Data Platform Migration Strategy
The data platform migration strategy varies depending on the scale and complexities of the environment.
Some of the strategies used include -
Big-bang migration
All workloads are moved in a defined period. This strategy is relatively quicker, but it also carries more risk.
Migration in phases
Workloads are phased and are moved in batches according to business priority, dependency, or data domain.
Hybrid Migration Strategy
Workloads are kept in Snowflake, while others are moved to Databricks using a hybrid strategy.
4. Data Migration and Transformation
It is the process of moving data from Snowflake to the target Databricks environment.
Depending on the design, the data will be migrated to the cloud object storage and then transformed to Delta Lake tables.
Validation should include -
- Row counts
- Data types
- Null values
- Duplicate records
- Partitioning
- Table relationships
- Historical data
- Data freshness
- Data quality rules
Data transformation logic may also need to be rewritten because SQL syntax, functions, procedures, and execution behavior can differ between platforms.
5. Rebuild Data Pipelines
Existing pipelines in Snowflake may use particular software, SQL engines, schedulers, and many other platform-specific features.
They should be analyzed first before trying to recreate them.
The following is a list of areas that might require re-engineering -
- Data ingestion processes
- Batch processes
- Stream processes
- Transformation processes
- Data quality processes
- Scheduling and orchestration
- Error handling
- Monitoring
The focus should be to build native Databricks pipelines that can be maintained along with business logic.
6. Migration of Security & Governance
Security and governance are crucial factors during the process of migration from Snowflake to Databricks.
The team must develop the strategy for permissions and policies for the target platform for the following aspects -
- User access
- Roles and groups
- Data ownership
- Sensitive data
- Row-level and column-level access
- Audit logging
- Data lineage
- Encryption
- Regulatory requirements
Consider setting up governance on Databricks before moving your workloads.
7. Testing and Validating the Migration
Testing is an ongoing process. It should not be considered a one-time event.
The two environments should be compared based on several factors.
- Ensure the accuracy of the data records, schema, data types, and transformations.
- Ensure that the applications, dashboards, reporting, and any other downstream applications are working well.
- It is necessary to ensure that the performance of the queries and pipelines is validated for the existing load.
- Ensure that users have access to resources they are authorized to.
- Ensure that the key reports and metrics are verified by the business stakeholders.
8. Cut Over to Databricks
With the workloads validated, the team can then plan for production cutover.
A good cutover plan must include -
- Migration timelines
- Data synchronization requirements
- Final data loads
- Application changes
- User communication
- Validation checkpoints
- Rollback procedures
- Post-migration monitoring
A rollback approach works well in a critical production environment.
How Custom Software Development Services Help Data Migration
Data migration processes may require not only adjustments to the platform settings but also some changes to the business applications, API, dashboards, and pipelines.
Custom software development services are useful for -
- Custom migration utilities
- Data integration pipelines
- API integrations
- Application modernization
- Data validation frameworks
- Workflow automation
- Custom monitoring solutions
- Legacy system integration
- Analytics application development
This approach is helpful, especially if an organization has a customized set of technologies that can’t be migrated using the regular platform tools.
Conclusion
Snowflake to Databricks data migration can be beneficial in establishing a unified platform for data engineering, analytics, and AI operations. The team must have adequate planning in all these aspects to make migration successful. It is suggested that engineering managers assess, set targets, prioritize workloads, phase the migration process, and validate each element before production cutover.
Fast migration is not always the best migration. The best migration is the one that creates a sustainable and integrated data platform that suits the long-term needs of the business.
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