Databricks Migration Services
As a Databricks Consulting Partner, dbSeer helps organizations move off aging data warehouses and scattered databases onto a single Databricks lakehouse that serves reporting, analytics, and AI from one governed foundation.
A good migration does more than move your data to a new place. It's your chance to build the platform your team will rely on for the next decade. That's why we start foundation-first: we map your sources, workloads, and reporting dependencies against where your business is headed before a single table moves.
Start With a Six-Week Data Platform AcceleratorWhy Organizations Are Migrating to Databricks
Legacy warehouses were built for a narrower job: structured data, scheduled reports, and a small group of people who knew how to query them. Today’s teams need more. They need structured and unstructured data in one place, governance that scales with access, and a foundation ready for AI use cases without a separate platform for each.
A Databricks lakehouse brings those needs together. Data engineering, BI, and machine learning run on the same data, which removes the copies and handoffs that cause reports to disagree. Governance is centralized instead of rebuilt system by system.
The platform alone won’t deliver those benefits. In a survey of more than 1,200 data management leaders, 63% of organizations either lacked, or were unsure whether they had, the right data management practices for AI. That’s why our approach starts with data quality, ownership, and governance, not just moving tables.
What We Migrate to Databricks
We migrate legacy and on-premises data warehouses, older big data clusters, and data scattered across cloud databases and departmental systems into one governed Databricks lakehouse. Every migration covers more than the data itself: tables and schemas, SQL logic and stored procedures, ETL and orchestration jobs, permissions, and the reports and dashboards that depend on them.
How We Build Your Databricks Lakehouse
Medallion Architecture
Your data is organized into bronze, silver, and gold layers, so raw data, cleaned data, and business-ready data each have a clear home and a clear owner. Your analysts know which tables to trust, and your engineers know where each fix belongs.
Governance From Day One With Unity Catalog
Migration is the best moment to fix access and governance, not something to patch later. We implement Databricks’ Unity Catalog from the start, giving you centralized permissions, data lineage, and auditability across tables, notebooks, dashboards, and models.
History You Can Analyze
Many legacy systems overwrite records when they change, so the prior state is lost. We design your lakehouse to preserve change history using Delta Lake's native capabilities, so your team can see how the business looked last quarter, not just today.
Analytics and AI Your Team Can Use
We connect your lakehouse to the BI tools your team already relies on, build Databricks AI/BI dashboards where they fit, and configure Genie spaces so business users can ask questions in plain English without writing SQL.
Built for Your Team to Own
We don't hand you a platform and walk away. You get documentation, runbooks, and hands-on training so your team can operate and extend the lakehouse. Through our DataOps Foundation, we can also monitor data quality continuously so issues surface upstream, before they undermine a report, dashboard, or AI application.
Our Databricks Migration Process
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Assess
Workshops to map your current environment, data sources, reporting dependencies, and business goals. You get a clear target architecture and a realistic timeline before any commitment.
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Mobilize
Detailed design and migration planning: medallion layers, governance model, data flows, and a sequenced plan that keeps your business running throughout the transition.
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Migrate and Modernize
We migrate in phased waves, running old and new environments in parallel where needed. Before each cutover, we reconcile data and business metrics, test access and performance, and get sign-off from your business owners. After cutover, we monitor the new environment, hand over documentation and runbooks, and retire legacy components against an agreed plan.
Start With the Data Platform Accelerator
Large migrations often stall when they begin without a clear scope, endpoint, or delivery model. Our Data Platform Accelerator makes the first step concrete: a fixed six-week engagement with a defined start, a defined endpoint, and a defined set of platform components.
You get a working, governed Databricks foundation built on your real data, along with a clear picture of what the full migration requires. You don’t sign up for an open-ended transformation program before either side understands the problem.
The Accelerator delivers the agreed platform foundation and initial workloads. The full migration is then scoped separately, covering remaining pipelines, reporting changes, validation, cutover, and legacy retirement.
Databricks Migration Results
ATCS: 30 Years of Data, One Governed Lakehouse
dbSeer unified 30 years of data for ATCS into a Databricks lakehouse with Unity Catalog, medallion architecture, and Power BI reporting. The result is AI-ready infrastructure their team trusts and builds on.
National Research Organization: From Internal Reporting to a Governed Platform in Six Weeks
A national research organization serving federal agencies brought dbSeer in to apply the same data rigor it uses for clients to its own internal operations. In a fixed six-week Data Platform Accelerator, we delivered an internal analytics platform on Databricks with governed pipelines, Unity Catalog role-based access, and AI/BI dashboards for leadership.
Databricks on AWS
As both a Databricks Consulting Partner and an AWS Advanced Consulting Partner, dbSeer often deploys Databricks on AWS. This connects your lakehouse to the AWS services, security model, and cloud commitments your organization already has. If your target architecture calls for a different cloud, we’ll design for that instead.
Databricks and Claude
Migration is the foundation. What you build on it is where the value shows up. Anthropic’s Claude models run natively in Databricks, so your team can build AI agents and applications directly on your governed lakehouse data, with Unity Catalog permissions applied to every query.
As a Databricks Consulting Partner and a Select tier member of the Claude Partner Network, dbSeer helps you take that next step. Through our AI Accelerator, we turn your newly migrated data into working AI use cases, starting with the ones that matter most to your business.
Frequently Asked Questions
It depends on the number of sources, the volume of data, and how many reports and pipelines need to move. Most organizations start with our six-week Data Platform Accelerator, which delivers a working, governed foundation and a clear roadmap for the full migration. We’ll give you a realistic timeline during the Assess phase, before any commitment.
Cost depends on the number of sources, the complexity of your pipelines and SQL logic, how many reports need to change, and how long old and new environments run in parallel. We establish a baseline of your current platform costs during the Assess phase, so you can compare the one-time migration investment against ongoing operating costs before you commit.
It depends on the workload. Some workloads should move quickly and be optimized later. Others are worth redesigning now, because carrying their problems into a new platform only moves the cost. We make that call workload by workload during the Assess phase, so speed and long-term value are balanced deliberately.
Not all of them. Some logic converts cleanly with automation. Some needs refactoring to run well on Databricks. Some is worth redesigning entirely. We inventory your pipelines up front, sort them into those groups, and test everything that’s converted before it goes live.
We map your reporting dependencies during the Assess phase and sequence the migration so reports keep working throughout. Where it makes sense, we rebuild reports on the new gold layer so they’re faster and draw on a single source of truth.
We plan around your availability requirements, using phased migration and parallel operation so your teams keep working throughout. Before each cutover, we agree on validation criteria, any required interruption, and a rollback plan.
Unity Catalog is Databricks’ unified governance layer. It gives you one place to manage permissions, track data lineage, and audit access across your lakehouse. Setting it up during migration means governance is built in from day one instead of added later.
Yes. As an AWS Advanced Consulting Partner and a Databricks Consulting Partner, we frequently deploy Databricks on AWS and integrate it with your existing AWS services and security model.
A governed lakehouse is a much stronger foundation for AI, but readiness depends on the use case. We assess the relevant data for quality, completeness, access, and traceability, then identify any additional preparation it needs. Once that’s in place, our AI Accelerator can also help you put it to work.
Ready to Migrate to Databricks?
Tell us about your current environment, and we'll show you what a foundation-first migration looks like for your team.
Not sure where to begin? Take our Data Assessment.