Navigating SQL Server to Databricks Migration in 2026

For decades, Microsoft SQL Server has been the backbone of enterprise data platforms as it is reliable, familiar, and deeply embedded in business processes. Yet as organizations accelerate their data, analytics, and AI-driven decisioning initiatives, the limitations of a monolithic, vertically scaled RDBMS are increasingly exposed.

At MSRcosmos, these inflection points show up in almost every engagement. Enterprises want to modernize their data estate, reduce total cost of ownership (TCO), and unlock AI, without disrupting mission-critical workloads. Migrating from SQL Server to the Databricks Data Intelligence Platform has emerged as a proven path to achieve all three.

This blog presents a 2026-ready, modern migration playbook, combining Databricks’ latest capabilities with MSRcosmos’ field experience in cloud data modernization and Lakehouse migration projects.

Why Modernize from SQL Server to Databricks?

The core shift is architectural. SQL Server was designed for traditional OLTP and limited analytics, optimized primarily for scale-up scenarios. Databricks, by contrast, is built on an Open Lakehouse architecture that separates compute and storage and scales out elastically, while adding a unified layer for AI and governed decision-making.

SQL Server vs Databricks: At a Glance

For enterprises running on-premises or IaaS-hosted SQL Server, this shift is not just about infrastructure. It is about moving from siloed data and rigid ETL pipelines to a single, governed Lakehouse and data intelligence platform that serves dashboards, applications, and AI models from the same source of truth.

Modern Migration Strategy for 2026

MSRcosmos recommends approaching SQL Server to Databricks migration as a structured, multi-phase program rather than a one-off “lift and shift”. The good news is the current generation of Databricks Lakeflow and SQL capabilities has significantly reduced migration friction.

At a high level, the journey typically follows these stages:

1.Discovery and assessment
2.Data ingestion and synchronization
3.SQL/T-SQL logic migration
4.Data modeling and Lakehouse design
5.Validation, optimization, and cutover

The following sections focus on the technical pillars that matter most for today’s Databricks environments.

Data Ingestion: From Manual ETL to Lakeflow

Historically, getting data out of SQL Server and into the cloud involved building and maintaining brittle ETL using SSIS, custom scripts, or third-party tools. That model no longer scales for modern data estates.

With Databricks Lakeflow, ingestion becomes a native, managed capability rather than a custom engineering problem. Lakeflow Connect for SQL Server provides:

• Initial load: Easily ingest historical SQL Server data into the Bronze layer of the Lakehouse without heavy custom code.
• Change Data Capture (CDC): Continuously capture inserts, updates, and deletes from SQL Server and apply them in near real time in Databricks tables.
• Operational simplicity: No ingestion servers to manage, patch, or tune reducing operational overhead and risk.

For MSRcosmos clients, this allows migration teams to focus on data modeling, performance, and business logic rather than building and maintaining extraction pipelines. Lakeflow’s place as the standard ingestion and transformation fabric also simplifies long-term operations.

Code and Logic Migration: AI-Assisted Refactoring

One of the biggest perceived blockers to migration is the volume and complexity of T-SQL like stored procedures, views, user-defined functions, and ETL logic built up over years.

In the current Databricks landscape, the approach to this challenge is:

• Automated baseline conversion: Tools such as Databricks-acquired code converters can transform schemas (tables, views) and many SQL constructs into Databricks-compatible SQL or PySpark.
• AI-assisted refinement: Databricks Assistant and other generative AI capabilities in the workspace help engineers iteratively refine translated logic, resolve incompatibilities, and optimize SQL for the Photon engine.

Instead of treating conversion as a manual rewrite, MSRcosmos positions it as a guided modernization process with the use of automation to do the heavy lifting, and apply expert oversight to align logic with Lakehouse best practices and performance standards.

Business Logic: Leveraging Native SQL Stored Procedures

Previously, migrating stored procedures often meant rewriting them into notebooks, Python jobs, or complex workflows that disrupts the established patterns and skill sets.

With native SQL stored procedures in Databricks SQL now generally available and widely adopted:

• Familiar constructs: Many procedural SQL patterns (variables, conditional logic, loops) can run directly in Databricks SQL.
• Gradual modernization: Teams can first lift-and-shift critical logic as stored procedures and then incrementally refactor to more modular, Lakehouse-aligned designs over time.

For enterprises with heavy procedural logic, this significantly reduces both risk and effort, while preserving business rules during the transition.

Target Architecture: Medallion Lakehouse for BI, AI, and Decisions

Once data and logic are in Databricks, the focus shifts to designing a target architecture that supports analytics, AI, and decision applications. MSRcosmos typically recommends the Medallion (Bronze–Silver–Gold) architecture:

• Bronze (Raw): Landing zone for ingested SQL Server data via Lakeflow, preserving source fidelity.
• Silver (Refined): Cleaned, conformed, and quality-checked data with standardized schemas and business rules.
• Gold (Curated): Aggregated, domain-specific data products optimized for BI, operational analytics, and AI features.

On top of this foundation:

• BI tools (Power BI, Tableau, etc.) connect to Databricks SQL Warehouses, often with improved performance and simpler data refresh patterns.
• Databricks’ own AI-powered BI and decision experiences can be adopted alongside or in addition to existing BI tools, giving organizations a path to more integrated analytics.
• AI/ML workloads leverage the same curated data in Gold and Silver layers, enabling a true “build once, use many times” paradigm across analytics and AI.

Unified governance through Unity Catalog ensures consistent security, lineage, and compliance across tables, dashboards, and models, and aligns with current Databricks governance best practices.

How MSRcosmos Orchestrates SQL Server to Databricks Migration

MSRcosmos combines Databricks platform capabilities with a proven services framework to de-risk and accelerate SQL Server modernization:

• Strategic Analysis: We conduct a comprehensive audit of your data structures, applications, and system dependencies. This data allows us to formulate a strong business justification and design a phased migration plan.
• Migration factory: Use automation for schema and code conversion, standardized Lakeflow-based ingestion, and repeatable patterns for Medallion modeling.
• Validation & optimization: We ensure data parity between systems, fine-tune query speeds, and implement essential compliance frameworks within your modern data architecture.
• Enablement: Upskill customer teams on Databricks, Unity Catalog, Lakeflow, and Lakehouse best practices to ensure long-term self-sufficiency.

This approach aligns technical execution with business outcomes like performance, cost optimization, agility, and AI readiness.

Ready to Modernize Your SQL Server Estate?

Migrating from SQL Server to Databricks is no longer just a technology upgrade, it is a strategic step toward building a future-ready data and AI platform that can evolve with your business.

With Lakeflow as the standard data engineering layer, AI-assisted code migration, and support for SQL stored procedures, the barriers to adoption are lower than ever. The key success factor now is a structured, experience-led approach.

If you are considering modernizing your SQL Server workloads, MSRcosmos can help you:

• Assess your current environment and build a pragmatic migration roadmap.
• Accelerate delivery using proven Databricks patterns, tools, and automation.
• Realize value quickly through optimized BI, analytics, and AI use cases on the Lakehouse and data intelligence platform.

Contact MSRcosmos today to schedule a Databricks migration assessment or workshop and take the next step in your 2026 data modernization journey.