Legacy Platform Migration

SQL Server to Databricks
Migration
Use Case

Enterprise Data Warehouse

Where SQL Server Migrations Break

Treating the migration as a database copy instead of a platform redesign

Underestimating schema, T-SQL, stored procedure, and SSIS/ETL complexity

Migrating obsolete logic and low-value workloads without reassessment

Ignoring downstream dependencies, security mapping, and validation

Carrying SQL Server-specific patterns directly into Databricks without refactoring

Mishandling Identity columns, primary key enforcement gaps, and dynamic SQL

We address these directly with a production-first approach.

Our SQL Server to Databricks Migration Approach

A structured, phased framework built from real delivery experience.

Discovery and Assessment

  • • Inventory of schemas, tables, stored procedures, SQL Agent jobs, SSIS packages, and dependencies
  • • Identification of migration scope, complexity, and downstream impacts using automated profiling

Migration Strategy

  • • Schema, data, and code migration planning
  • • Phased migration design with wave-based prioritization instead of blind lift-and-shift
  • • Architecture, security, and tooling alignment

Build on Databricks

  • • Delta Lake implementation
  • • Data ingestion and transformation pipelines
  • • Lakehouse data model (bronze, silver, gold)
  • • Unity Catalog for governance and access control

Validation and Cutover

  • • Data reconciliation and testing
  • • SLA validation
  • • Parallel run and controlled cutover

Optimization

  • • Query and pipeline tuning
  • • Cost and workload optimization
  • • Governance, monitoring, and operational hardening

This approach reflects best practices from real migration delivery playbooks.

Use Case

SQL Server to Databricks Migration
for an Enterprise Data Warehouse

A large enterprise was operating a complex SQL Server environment supporting critical reporting, analytics, and operational workflows.

Over time, the platform became a bottleneck.

Long-running batch jobs and SQL Agent processes impacting daily operations

High infrastructure and licensing costs including SQL Server Enterprise Core licenses

Complex, tightly coupled SSIS pipelines and stored procedure logic

Limited ability to support advanced analytics and AI initiatives

The organization needed to modernize without disrupting mission-critical systems.

What We Did

KData led the migration to Databricks, starting with a full discovery of data assets, schemas, pipelines, T-SQL workloads, SSIS packages, and dependencies.

Assessed and prioritized schemas, tables, T-SQL workloads, stored procedures, and SQL Agent jobs

Migrated schema and data into a Databricks lakehouse architecture using bronze, silver, and gold layers

Refactored T-SQL, SSIS ingestion logic, and transformation pipelines for Databricks using Spark SQL and PySpark

Implemented governance, access control, and auditability using Unity Catalog

Executed a phased migration with validation, parallel runs, and controlled cutover

Outcome

The result is not just a migration, but a production-ready Databricks platform.

Improved pipeline performance and reliability

Reduced platform complexity and operational overhead

Enabled a unified foundation for analytics, reporting, and AI

The transition was executed without disrupting core business operations.