Synapse to Databricks: The Migration Playbook

Azure Synapse customers are increasingly finding themselves juggling separate Dedicated SQL, Serverless SQL, and Spark pools, alongside tools like Azure Data Factory. This fragmented approach leads to duplicated governance, extra tooling costs, and operational headaches, especially for a platform not originally designed for modern AI and streaming workloads. A practical guide, detailed in the Databricks blog, outlines a field-tested playbook for migrating to a unified Databricks Lakehouse, governed by Unity Catalog.

Organizations that built on Synapse made a sensible choice for SQL analytics at the time. However, the platform's data warehouse-centric design struggles to meet the demands of today's data teams, which increasingly focus on machine learning, real-time pipelines, and AI applications. This often necessitates adding more services and integrations, increasing complexity and operational overhead.

The primary drivers for migrating from Synapse to Databricks are clear: a unified data estate, future readiness for AI, and improved operational efficiency. Databricks consolidates data engineering, analytics, machine learning, and governance onto a single platform. This eliminates the need to switch between services with different operating models, reducing complexity and integration points. The move promises a simpler architecture, faster data delivery, and lower costs, as seen with companies like Casey's and Italgas.

Understanding the Migration Scope

A common pitfall in a Synapse migration is underestimating the scope. Synapse is not a monolithic platform but a collection of distinct services, each requiring tailored migration strategies.

Dedicated SQL Pools represent the most complex component. Migrating these involves moving years of accumulated business logic, stored procedures, distribution strategies, and optimizations. This effort extends to orchestration (ADF/Synapse Pipelines), permissions management (SQL permissions, Purview), and BI/third-party connectivity.

Serverless SQL Pools are generally simpler, primarily acting as a query layer over data lake files. Migration typically involves re-establishing views and external tables.

Spark Pools are the easiest to migrate, as both Synapse Spark and Databricks are built on Apache Spark, often allowing notebooks to move with minimal changes.

These components move at different speeds and involve different stakeholders, making a phased, structured program essential rather than a single, undifferentiated project.

Structuring the Synapse Migration

A successful Synapse migration requires a structured program, not just a technical project. This involves several key phases:

• Discovery: Tools like Lakebridge Profiler scan the Synapse estate to collect metadata on configuration, resource utilization, and query patterns for TCO analysis.
• Assessment: Lakebridge Analyzer evaluates T-SQL code, classifying objects by complexity, flagging unsupported constructs, and mapping dependencies to estimate timelines and define migration priorities. Start with lower-complexity workloads.
• Design: Decide on a hybrid approach, automating bulk code conversion while modernizing incrementally. A BI-first strategy, exposing Synapse data via Lakehouse Federation before pipeline migration, can deliver early business value.
• Pilot: Validate the migration strategy end-to-end with a lighthouse use case, migrating it from ingestion to consumption and cutting over to production. This produces reusable assets for subsequent migration waves.
• Migration in Waves: Execute the scaled migration in waves, each delivering a visible business win. Parallel workstreams for ingestion, transformation, orchestration, and consumption ensure early value delivery and predictable timelines for retiring Synapse.

Databricks offers support through its Forward Deployed Engineering team, certified partners, and accelerators like Lakebridge to automate heavy lifting and build sustainable operating models.

Data Ingestion and Code Conversion

Before code conversion, data must be ingested into the lakehouse. Databricks offers managed ingestion via Lakeflow Connect or supports third-party tools like Fivetran and Airbyte, which can ingest data directly into Delta Lake.

Code conversion is typically the most complex phase, with automated tooling handling 80-90% of the translation. Refining procedural logic and resolving untranslatable patterns require manual effort. Key differences in syntax between Synapse and Databricks need careful attention during this process. This is where a smooth Synapse migration to Databricks becomes critical for achieving a truly unified data estate, as discussed in the context of Azure Databricks' advancements.