Databricks · Snowflake · Data Fabric migration
MIGRATEAi
An enterprise agentic AI accelerator that moves legacy data warehouses to the Databricks Lakehouse discovering, converting, validating, and optimizing at code scale, with engineers authorizing every consequential change.
Databricks · Snowflake · Data Fabric migration
The Challenge
A converted query isn't a migration. A validated cutover is.
Legacy warehouses hold decades of business logic thousands of SQL procedures, tangled ETL, and undocumented dependencies. Handmigrating that estate is slow, error-prone, and hard to trust: teams rewrite code line by line, discover hidden logic late, and only reconcile data at the very end, when defects are most expensive to fix.
10,000s
SQL procedures & ETL jobs in a typical enterprise estate
40–60%
Migration cost is manual code conversion & testing
99.9%+
Data accuracy needed to earn cutover trust
18–24 mo
Typical legacy migration timeline without automation
Why traditional migrations stall
01
Manual conversion at scale
SQL, stored procedures, and ETL are rewritten by hand. The work is slow, inconsistent, and impossible to repeat across a large estate.
02
Unknown dependencies
Lineage is undocumented and business logic is buried in code. Nobody knows what a change will break until it breaks in production.
03
Validation as an afterthought
Reconciliation happens only at the end. Data defects surface at cutover the latest, riskiest, most costly moment to find them.
The Operating Model
Three layers that turn legacy code into a governed lakehouse
Migrate AI runs as three integrated layers over a Unity Catalog governance foundation and a medallion architected Delta Lake. Together they form a closed loop from what you have, to how it moves, to proof it’s right with engineers wrapped around every consequential decision.
01
Discovery & Assessment
Scans metadata, code, and schedulers to build a complete inventory, lineage, and effort model.
Metadata & lineage discovery
Dependency graph
Complexity & effort scoring
02
Conversion & Optimization
AI transpiles SQL and ETL into Databricks-native artifacts, then tunes them for Lakehouse performance.
SQL & stored-proc conversion
ETL → DLT & PySpark
Photon / ZORDER / Liquid tuning
03
Validation & Cutover
Row / checksum reconciliation
Automated regression testing
Unity Catalog & lineage
The Agent Inventory
Eight agents. Each with a trigger, a boundary, and a hand-off
Migrate AI is a multi-agent system. Agents run in parallel through an orchestration framework and share state through the metadata and lineage graph. No agent has autonomous authority over schema changes, production deployment, or cutover that boundary is enforced in the architecture.
Discovery Agent
Scans metadata, SQL, ETL packages, and schedulers; builds inventory, data lineage, and the dependency graph.
Assessment Agent
Scores object complexity and effort; estimates DBUs, TCO, and migration risk to prioritize the roadmap.
SQL Conversion Agent
Transpiles SQL, stored procedures, views, UDFs, and recursive logic into Databricks SQL and Spark SQL.
ETL Conversion Agent
Converts Informatica, SSIS, DataStage, Talend, and Ab Initio into Delta Live Tables, PySpark, and Workflows.
Optimization Agent
Applies partitioning, ZORDER, Liquid Clustering, Photon, and caching for Lakehouse cost and performance.
Validation Agent
Reconciles source and target by row count, checksum, and business rules; emits evidence-cited exception reports.
Governance Agent
Provisions Unity Catalog, access controls, data classification, and lineage; enforces policy before cutover.
Documentation Agent
Generates data dictionaries, runbooks, lineage diagrams, and migration reports never codes autonomously
The Migration Lifecycle
Ten stages, one closed loop from legacy to lakehouse.
Every migration follows the same loop: an estate is discovered and prioritized, code is converted and validated, workloads are optimized and governed, then cut over and continuously improved each stage gated by validation before the next begins.
Value Realization
Baselines and targets across speed, quality, and cost.
Progress is tracked on a live migration dashboard across efficiency, quality, and platform dimensions the basis for status reporting, cost savings validation, and cutover readiness.
Migration Efficiency
| Metric | Manual Baseline | With Migrate AI |
|---|---|---|
| Code automation rate | < 20% | 70–90% |
| Migration timeline | 18–24 months | 8–12 months |
| Overall migration effort | Baseline | 50–70% Lower |
| Testing effort | Fully Manual | 60–80% Reduced |
| Data reconciliation accuracy | Spot Checks | 99.9%+ |
| Documentation coverage | Partial / Manual | 90%+ Automated |
Platform Outcomes
| Metric | Legacy Warehouse | Lakehouse Target |
|---|---|---|
| Total cost of ownership | Baseline | 25–40% Lower |
| Query performance | Manual Tuning | Photon-accelerated |
| Governance | Fragmented | Unified (Unity Catalog) |
| Self-service analytics | Limited | AI/BI Enabled |
Frequently Asked Questions
MigrateAi is an enterprise agentic AI accelerator that moves legacy data warehouses to the Databricks Lakehouse. Eight purpose-built agents discover, convert, validate and optimize your estate at code scale, with engineers authorizing every consequential change.
SQL, stored procedures, views, UDFs and recursive logic are transpiled into Databricks SQL and Spark SQL. ETL built in Informatica, SSIS, DataStage, Talend and Ab Initio is converted into Delta Live Tables, PySpark and Databricks Workflows, using reusable conversion templates.
MigrateAi targets a 70–90% code automation rate, compared with under 20% for a typical manual migration. Everything the agents convert is reviewed by your engineers before it is committed, and complex logic is flagged for human attention rather than guessed.
No. No agent has autonomous authority over schema changes, production deployment or cutover. That boundary is enforced in the architecture. The Documentation Agent never writes code autonomously, and every cutover is governed and reversible.
The Validation Agent reconciles source and target continuously, not only at the end, using row counts, checksums and business rules, together with automated regression testing. Every exception comes with an evidence-cited report, with a target of 99.9%+ reconciliation accuracy before cutover.
Typical legacy migrations take 18–24 months without automation. MigrateAi targets 8–12 months by automating conversion and testing. Every migration follows the same ten-stage lifecycle, and each stage is gated by validation before the next begins.
The Assessment Agent estimates DBUs and total cost of ownership up front, and the Optimization Agent applies partitioning, ZORDER, Liquid Clustering, Photon and caching to converted workloads. The target is 25–40% lower total cost of ownership than the legacy warehouse.
The Governance Agent provisions Unity Catalog, access controls, data classification and lineage, and enforces policy before cutover, so governance is unified from day one instead of fragmented across legacy tools.
The Documentation Agent generates data dictionaries, runbooks, lineage diagrams and migration reports from the migration itself, targeting 90%+ automated documentation coverage.
Yes. Try the interactive demo to follow a migration wave from discovery to cutover, or request a demo to see MigrateAi run against your own warehouse estate.