Legacy Platform Migration

Teradata to Databricks
Migration
Use Case


Where Teradata Migrations Break

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

Underestimating BTEQ, FastLoad, MultiLoad, stored procedure, and macro conversion complexity

Migrating obsolete workloads and low-value pipelines without reassessment

Ignoring downstream BI dependencies, security mapping, and validation requirements

Carrying Teradata-specific patterns like Primary Index and BTEQ status variables directly into Databricks without refactoring

We address these directly with a production-first approach.

Our Teradata to Databricks Migration Approach

A structured, phased framework built from real delivery experience.

Discovery and Assessment

  • • Inventory of databases, BTEQ scripts, stored procedures, macros, and FastLoad and MultiLoad utilities, and downstream dependencies
  • • Identification of migration scope, complexity, and SQL dialect conversion requirements

Migration Strategy

  • • DDL, schema, data, and code migration planning
  • • Phased migration design with incremental load approach for the cutover window
  • • Architecture, security, Unity Catalog, and tooling alignment

Build on Databricks

  • • Convert Teradata DDLs to Databricks DDLs with data type mapping
  • • Migrate Teradata SQL, BTEQ, stored procedures, macros, FastLoad, and MultiLoad to Databricks SQL and Python notebooks
  • • Delta Lake implementation with bronze, silver, and gold lakehouse layers
  • • Databricks Workflows for orchestration and scheduling
  • • Unity Catalog for governance and access control

Validation and Cutover

  • • Row count, aggregate, and row-by-row data reconciliation
  • • SLA validation against Teradata source results
  • • Parallel run and controlled cutover to production

Optimization

  • • Query and pipeline tuning using liquid clustering and predictive optimization
  • • Cost and workload optimization
  • • Governance, monitoring, and operational hardening

This approach reflects best practices from real Teradata migration delivery playbooks.

Use Case

Teradata to Databricks Migration

A major North American organization was operating a large-scale Teradata environment supporting critical reporting, operations, and planning workflows.

Over time, the platform became a bottleneck.

Long-running BTEQ batch jobs impacting daily operations and missing SLAs

High AMP-based infrastructure and licensing costs

Complex, tightly coupled FastLoad, MultiLoad, and stored procedure pipelines

Limited ability to support advanced analytics and AI initiatives

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

What The Team Did

The migration to Databricks started with a full discovery of databases, BTEQ scripts, stored procedures, macros, FastLoad and MultiLoad utilities, and downstream dependencies.

Assessed and prioritized tables, SQL workloads, BTEQ scripts, stored procedures, macros, and ETL jobs

Converted Teradata DDLs, SQL, BTEQ, stored procedures, FastLoad, and MultiLoad to Databricks using automated tooling

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

Implemented governance, access control, and auditability using Unity Catalog

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

Outcome

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

Improved pipeline performance and reliability after elimination of BTEQ batch bottlenecks

Reduced platform complexity and elimination of Teradata licensing costs

Enabled a unified foundation for analytics, reporting, and AI use cases

This transition was executed without disrupting core business operations.