Navigating a Synapse Migration to Databricks

2026-07-08 · Source: Databricks · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

Migrating from Azure Synapse to Databricks addresses the limitations of warehouse-centric architectures, offering a unified data estate, future readiness for AI/ML, and operational efficiency. This transition consolidates data engineering, analytics, and machine learning on a single platform, reducing complexity and integration points. Organizations like Casey's saw operational data delivery times halved from eight to four hours, while Italgas reported a 73% reduction in workload costs. The migration process is structured into discovery, assessment, design (often hybrid), pilot, and wave-based execution, leveraging tools like Lakebridge Profiler and Analyzer. Key technical aspects include data ingestion via Lakeflow Connect or third-party tools, and T-SQL code conversion, which automates 80-90% of the process, focusing on removing physical directives and remapping functions.

Key takeaway

For MLOps Engineers or Data Architects planning a platform modernization, migrating from Azure Synapse to Databricks offers significant operational simplification and AI readiness. You should structure the migration as a phased program, using automated tools like Lakebridge for code conversion and focusing engineering effort on complex logic and thorough validation. Plan for change management and operational readiness, avoiding early decommissioning to ensure a smooth transition and build team confidence.

Key insights

Unifying data, analytics, and AI on Databricks simplifies complex Synapse environments and prepares for future workloads.

Principles

• Treat migration as a structured program, not a single project.
• Automate code conversion aggressively (80-90%).
• Prioritize validation, often consuming more effort than migration.

Method

Structure migration into discovery, assessment, design (hybrid/BI-first), pilot, and wave-based execution, using tools like Lakebridge Profiler/Analyzer.

In practice

• Use Lakebridge Profiler/Analyzer for initial scope and complexity.
• Implement Liquid Clustering with CLUSTER BY AUTO for performance.
• Run reconciliation after each migration wave.

Topics

• Azure Synapse Migration
• Databricks Lakehouse
• Data Platform Modernization
• T-SQL Conversion
• Data Governance
• Operational Efficiency

Code references

• databrickslabs/lakebridge

Best for: Data Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.