Azure Synapse got a lot of enterprises through their first real cloud data warehouse. But teams that built on it are now stitching together Dedicated SQL Pools, Serverless SQL, Spark Pools, and Azure Data Factory just to keep the lights on, and none of those pieces were built for machine learning or streaming. An Azure Synapse to Databricks migration replaces that patchwork with one lakehouse governed by Unity Catalog. This guide covers what actually moves, how to plan it, how to convert the SQL, and how to validate the result before cutover.
What Is Azure Synapse Analytics?
Azure Synapse Analytics is Microsoft’s platform for querying and processing data at scale. It bundles Dedicated SQL Pools for provisioned data warehousing, Serverless SQL Pools for on-demand querying over a data lake, Spark Pools for big data processing, and Synapse Pipelines for orchestration, all in one Azure workspace. For years that was a reasonable way to run BI workloads without managing separate infrastructure for each piece. The strain shows once data teams add machine learning, streaming, and AI use cases the platform was never built to carry.
Why Migrate From Azure Synapse to Databricks?
Three reasons come up again and again. Unification: Synapse spreads governance and permissions across four or five services, and each one is another thing that can break. AI readiness: Synapse was built for BI, not the machine learning and generative AI workloads enterprises now expect the same data to support. Cost: licensing rarely drives the savings case, running fewer systems does. The market is already moving this way. 55% of enterprises plan to increase their data and analytics purchasing over the next 6 to 12 months, according to Futurum’s 1H 2025 Data Intelligence, Analytics, and Infrastructure Decision Maker Survey of 839 senior data and analytics practitioners (Brad Shimmin, Futurum, 2025). The payoff shows in practice too. Casey’s, the third-largest convenience store chain in the US, cut its operational data delivery time from eight hours to four after moving to Databricks Lakehouse, according to Databricks’ 2026 field playbook for Synapse migrations, and Italgas removed Synapse and Azure Analysis Services entirely, reporting a 73% drop in workload costs while serving Power BI and AI analytics straight from Databricks (Databricks, 2026).
What Needs to Be Migrated From Azure Synapse to Databricks?
Azure Synapse looks like one platform, but it’s really several services wearing the same badge, and each migrates differently.
Data and Tables
Every table, schema, and file in the Synapse data warehouse and connected data lake has to land in Delta Lake, along with the partitioning and retention rules that govern it.
Dedicated and Serverless SQL Workloads
Dedicated SQL Pools carry the heaviest lift: years of stored procedures, distribution keys, and indexing choices. Serverless SQL Pools are lighter, mostly views and external tables over the lake.
Synapse Spark Workloads
Synapse Spark and Databricks both run on Apache Spark, so notebooks and jobs usually move with only minor changes to configuration and library versions.
Synapse Pipelines and Data Integration
Pipelines built in Synapse or Azure Data Factory need rebuilding or repointing, whether that means Lakeflow Connect or an existing ETL tool talking to Delta Lake.
Notebooks and Custom Code
Custom Python, Scala, or SQL notebooks come along too, usually needing only light edits once running against Databricks.
Security and Governance
Permissions once split between SQL roles and Microsoft Purview get rebuilt as one model in Unity Catalog, covering data, notebooks, and AI assets under a single policy.
BI and Downstream Dependencies
Power BI reports, semantic models, and third-party tools wired into a Synapse endpoint need new connection strings and, sometimes, new authentication.
How Do Azure Synapse Components Map to Databricks?
Mapping each Synapse service to its Databricks counterpart makes the scope concrete before any code moves.
| Synapse Component | Databricks Equivalent |
| Dedicated SQL Pools | Databricks SQL Warehouses + Delta Lake |
| Serverless SQL Pools | Databricks SQL Warehouses (Lakehouse Federation for interim access) |
| Spark Pools | Databricks Jobs and Notebooks (Apache Spark) |
| Azure Data Factory / Synapse Pipelines | Lakeflow Connect + Databricks Workflows |
| Microsoft Purview + SQL permissions | Unity Catalog |
| Synapse Studio notebooks | Databricks Notebooks |
Distribution keys and clustered columnstore indexes have no direct equivalent; Delta Lake’s automated optimization replaces them instead.
How to Plan a Synapse to Databricks Migration
Planning is where most migration risk either gets caught early or gets baked into the schedule.
Inventory the Existing Synapse Environment
Catalog every pool, pipeline, notebook, and downstream connection actually in use. Profiling tools can scan the environment and build that inventory automatically.
Assess Workload Complexity and Dependencies
Classify each object by complexity and flag what won’t convert automatically, cursors and dynamic SQL especially, so the estimate reflects reality.
Define the Target Databricks Architecture
Decide how catalogs, schemas, and workspaces will be organized in Unity Catalog before migrating a single table.
Prioritize Workloads for Migration
Rank workloads by business value and complexity. Low-complexity, high-visibility workloads first builds momentum for the harder ones.
Define the Migration and Cutover Strategy
Decide upfront: big bang cutover or a phased migration by workload wave. That choice shapes testing and rollback planning.
How to Migrate From Azure Synapse to Databricks
Execution splits into ingestion, transformation, orchestration, and consumption. Running those four workstreams in parallel keeps the timeline realistic.
Set Up the Databricks Environment
Provision the workspace, configure Unity Catalog, and set the networking and security baseline before any data moves.
Migrate Data and Tables
Move data into Delta Lake using Lakeflow Connect or an existing ETL tool, keeping schemas and partitioning consistent with the target design.
Convert SQL and T-SQL Workloads
Automated tooling converts the bulk of T-SQL to Databricks SQL. What’s left, mostly procedural logic, needs a person’s judgment.
Migrate Spark Workloads and Notebooks
Port Spark notebooks over, update library versions and cluster configurations, and retest against the new environment.
Migrate or Modernize Data Pipelines
Rebuild Azure Data Factory and Synapse Pipeline workflows in Lakeflow Connect or Databricks Workflows, or modernize them incrementally once running.
Migrate Security and Governance to Unity Catalog
Recreate permissions, row and column level security, and lineage under Unity Catalog’s single policy model instead of splitting them across SQL roles and Purview.
Reconnect BI and Downstream Applications
Repoint Power BI, semantic models, and third-party tools to Databricks SQL Warehouses and update authentication where needed.
Validate Migrated Data and Workloads
Run reconciliation against the source system before decommissioning anything. Don’t skip this step to save time.
How Can a Synapse to Databricks Migration Accelerator Help?
An azure synapse to databricks migration accelerator exists because manual assessment and code conversion don’t scale past a handful of tables. Tools like Databricks Lakebridge scan the Synapse estate, classify every object by complexity, and automate the bulk of the SQL conversion, handling roughly 80 to 90% of the translation work, according to Databricks’ own field playbook for Synapse migrations (2026). That leaves engineers to focus on the harder 10 to 20%: cursors, dynamic SQL, edge cases that need judgment, not pattern matching. An accelerator also standardizes reconciliation after every wave.
How Should SQL and Code Be Converted During Migration?
Code conversion is usually the longest phase, and most of the pain comes from a handful of predictable patterns.
T-SQL and Databricks SQL Differences
Most T-SQL functions map directly: GETDATE() becomes CURRENT_TIMESTAMP(), TOP becomes LIMIT. Subtler issues cause more damage. Synapse’s SQL Pools are case-insensitive by default; Databricks SQL isn’t, so comparisons relying on that behavior can return different results after the move.
Stored Procedures and Functions
Databricks supports stored procedures natively, so structure and logic usually carry over intact. The real complexity is inside the procedure: cursors and row-by-row processing need rewriting, not translation.
Synapse-Specific Physical Design
HASH distribution, ROUND_ROBIN, and clustered columnstore indexes have no Databricks equivalent and get dropped. Delta Lake’s Liquid Clustering and automated file optimization take over that job without manual tuning.
Notebook and Spark Code Compatibility
Since Synapse Spark and Databricks both run Apache Spark, notebooks generally need library and configuration updates, not a rewrite.
How Do You Validate a Synapse to Databricks Migration?
Validation often takes longer than the migration itself. Skip it, and bad data quietly ends up in the new lakehouse.
Data Reconciliation
Compare row counts, aggregations, and hash-based checksums between Synapse and Databricks for every table, not just the ones that seem important.
Query and Business Logic Validation
Rerun converted queries and procedures against both environments and compare output, since a query that runs without error can still return the wrong answer.
Pipeline Validation
Confirm every migrated pipeline processes the same volumes on schedule and handles failures the way the original did.
Performance Testing
Benchmark query and job performance against Synapse baselines before cutover, so a regression is caught in testing, not production.
BI and Reporting Validation
Check that dashboards and reports return the same numbers end to end. A broken connection string is easy to miss until a business user notices.
What Are the Common Challenges in Synapse to Databricks Migration?
The challenges rarely show up where teams expect them. Synapse looks like one platform but is really several wearing the same badge, and treating it as one workstream is how schedules slip. Case sensitivity is a quiet one: Synapse’s SQL Pools default to case-insensitive matching, Databricks SQL doesn’t, so old logic can return different results after conversion. Physical design habits, distribution keys and clustered columnstore indexes chief among them, don’t map to Delta Lake and need unlearning, not translating. Governance sprawl is another: permissions split between SQL roles and Purview need rebuilding into one Unity Catalog model. And most Synapse teams are new to Databricks, so skipping training is a fast way to miss adoption targets.
What Are the Best Practices for Synapse to Databricks Migration?
Run assessment before conversion, so the real scope is clear before anyone writes converted code. Automate aggressively and save engineering time for the 10 to 20% that needs judgment: cursors, dynamic SQL, anything Synapse-specific. Reconcile after every wave instead of waiting until the end. Keep the legacy environment paused, not deleted, until the new platform proves itself in production. Treat migration and training as one project, since a platform nobody knows how to use doesn’t deliver the business case it was funded on.
How Should Organizations Plan the Cutover to Databricks?
A big bang cutover works for smaller, well-understood estates: one weekend, one switch, done. Most Synapse environments are too tangled for that and do better with a phased cutover by workload wave. Run both environments in parallel for business-critical workloads first, comparing outputs side by side while users keep working off familiar reports. Build a rollback plan per wave, and keep Synapse compute paused, not decommissioned, until the new workload runs clean for a full business cycle. Watch performance and data freshness closely in the weeks after cutover, since that’s when regressions surface.
How HoonarTek Helps Enterprises Migrate From Synapse to Databricks
HoonarTek runs Synapse to Databricks migrations as a structured program, not a one-off project. That starts with discovery and complexity assessment across Dedicated SQL, Serverless SQL, Spark Pools, and every pipeline feeding them, so the plan reflects what’s actually running, not what the architecture diagram says. HoonarTek’s data engineering team then handles code conversion, Unity Catalog governance design, and pipeline modernization, with reconciliation built into every wave, not bolted on at the end. The goal isn’t just to get workloads off Synapse. It’s a lakehouse the team understands well enough to run on their own, backed by a partner that stays through cutover and validation.
Frequently Asked Questions About Synapse to Databricks Migration
Can You Migrate Azure Synapse to Databricks?
Yes. Databricks publishes a field-tested playbook for moving Dedicated SQL, Serverless SQL, and Spark Pools into one Databricks Lakehouse, and accelerator tools handle most of the code conversion automatically.
How Long Does a Synapse to Databricks Migration Take?
It depends on the estate’s size and how much logic sits in stored procedures. A single-domain pilot can run six to eight weeks; a full enterprise migration runs several months in waves.
Do Synapse Pipelines Need to Be Migrated to Databricks?
Yes, unless the plan is to keep Azure Data Factory running alongside Databricks indefinitely. Most organizations rebuild pipelines in Lakeflow Connect or Databricks Workflows instead of maintaining two orchestration layers.
Can Synapse SQL Workloads Be Migrated to Databricks SQL?
Most T-SQL converts automatically, and native stored procedure support means structure and logic usually carry over. What’s left is mostly cursors, dynamic SQL, and Synapse-specific physical design.
Can Azure Data Factory Remain After Migrating to Databricks?
Technically yes, but running it alongside Databricks Workflows long term just recreates the sprawl the migration was meant to fix.
How Can Organizations Reduce Downtime During Synapse to Databricks Migration?
Migrate in waves, run parallel environments for business-critical workloads before cutover, and keep Synapse compute paused as a rollback option instead of decommissioning it immediately.
