Datafold Joins the Exclusive Databricks Delivery Provider Program to Automate Migrations
Datafold has joined the Databricks Delivery Provider Program — an invitation-only program for partners who meet Databricks' standards for delivering migrations. Fixed price, guaranteed timeline, proven data quality.
We’re proud to share that Datafold has joined the Databricks Delivery Provider Program, an invitation-only program reserved for partners who meet Databricks’ standards for delivering migrations to the Data and AI Platform. This achievement marks an exciting new chapter in our partnership with Databricks as we collaborate to help their customers rapidly modernize legacy infrastructure.
The new partnership creates an unparalleled value proposition for customers considering migration to Databricks, enabled by Datafold’s AI-first migration approach:
Fixed price Transparent pricing based on the volume of legacy objects, quote ready in 24 hours.
Guaranteed timeline Datafold contractually ensures the migration is completed on time, enabling customers to make the transition to Databricks in time for legacy renewals.
Proven data quality Legacy-to-Databricks value-level validation of every data object ensures smooth UAT and fast business adoption of the new platform.
Transacting on Databricks paper Customers can purchase migrations directly from Databricks, eliminating a procurement step and simplifying vendor management.
For enterprise teams that have been watching Databricks from a distance, hesitant about what migration would actually cost or how long it would take, this changes the calculus.
“Datafold made jaws drop. Their AI-driven ‘shrink the codebase’ magic is turning millions of lines into a fraction of the footprint.”
— Vijay Anala, Global Partner Migration Program Leader at Databricks
Why Databricks, why now
Databricks just crossed $5.4 billion in annualized revenue, growing 65% year over year. At a $134 billion valuation, it has become one of the defining infrastructure companies of this era — and it’s still early. Databricks aims to win the $149B total addressable market spanning data engineering, analytics, machine learning, and AI.
But growth at that scale runs into a stubborn problem: most enterprise data lives somewhere else. Legacy data platforms such as Informatica, Teradata, Oracle, and SQL Server collectively account for over $50B in the data analytics market.
Moving data is the easy part
Getting data into Databricks isn’t the bottleneck. The platform’s lakehouse architecture handles ingestion well, and data is fundamentally portable. The hard part is the code: pipelines, transformations, stored procedures, ETL mappings, macros. Business logic built years ago by people no longer at the company, often spanning a dozen different systems, without a trace of documentation.
You can’t just copy and paste that into Spark. You have to understand it, then modernize it — rewrite it into the optimal Databricks patterns.
At enterprise scale, that can mean millions of lines of code spanning multiple programming languages, including various flavors of SQL, Java, and proprietary DSLs.
This is where most migrations fall apart.
The old playbook isn’t working
Historically, any migration had two components:
Tools
Existing migration tools — known as “accelerators” or “transpilers” — attempt to automatically convert SQL dialects and procedural logic across platforms, but they work only on simple, clean code. Enterprise code is neither. Transpilers hit a wall fast, and building new ones for each source system is expensive and brittle. AI opened up new possibilities here, but popular AI agents and IDEs, while useful for individual engineers, struggle to operate at the scale and complexity required for a full data platform migration.
Humans
The other traditional approach is hiring a system integrator — a consulting firm that staffs a team of project managers, data architects, and engineers on a billable-hours basis. The problems are structural: more complexity means more people, more people mean harder coordination, and a model where finishing faster means earning less has a built-in conflict of interest. Even with AI layered on top, SI-delivered migrations routinely go over time and over budget — not as an exception, but as the industry standard.
What we do differently
#1: Outcome-based business model
Datafold doesn’t charge by the hour. We quote a fixed price with a fixed timeline, against a defined outcome, with guaranteed quality in terms of legacy and target system parity.
We can turn around a quote and start a project within 24 hours of first contact.
Our customers have told us that our timelines and costs are 5-6x better than the next-best alternatives they could find.
#2: Specialized AI agents
Technology makes a radically better performance possible.
Instead of starting with a team of humans and asking how AI can make them do the work faster, we start with AI doing ALL the work. Humans get involved only where absolutely necessary.
Our migration stack consists of three layers that work together:
- The Migration Agent handles code conversion at scale, going well beyond surface-level SQL translation to understand and rewrite complex legacy patterns.
- The Data Knowledge Graph maps the relationships across an entire data platform: the dependencies, lineage, and ontology (semantics) that make the Migration Agent understand customers’ data environment better than anyone else on their own team.
- The QA Layer, powered by value-level cross-database Data Diff, continuously validates outputs, so the Agent can catch and remediate issues automatically.
What’s next
If you’re an enterprise moving legacy workloads to Databricks — we’d like to talk. We’ll give you a straight answer on timeline and cost, without the weeks of scoping and handwavy caveats. Even if your migration has already started, if you are not happy with the pace and rising costs, we can turn that around.
With Datafold, migrations take weeks, not years.
