Why Modernize Your Data Platform Now

Many enterprise data teams are still running critical workloads on a mix of Hadoop clusters, cloud warehouses such as Snowflake, and on‑premises EDWs such as Teradata, Oracle, and SQL Server. Those systems are often stitched together from years of homegrown ETL, and as data volumes grow, that environment is expensive to maintain and difficult to govern.

Staying on this patchwork of legacy platforms usually means rising license and infrastructure costs. There are also long refresh cycles that keep dashboards a day behind the business and fragmented governance, making it burdensome to roll out new analytics or AI initiatives with confidence. Instead of building new capabilities, teams spend time reconciling numbers and firefighting pipeline issues, and every new project has to navigate platform constraints before it can show real value.

Why Databricks Is the Right Landing Zone

A Databricks Lakehouse, together with the broader Data Intelligence Platform, gives you a single environment for working with your data end to end. BI and self‑service analytics sit on that same foundation as machine learning, so teams are no longer jumping between disconnected systems.

When Hadoop, Snowflake, and legacy EDWs are consolidated into Databricks, data sprawl shrinks and pipelines are simpler to design and operate. This results in a governed layer built for streaming and advanced analytics, with AI workloads running on top of the same platform instead of on separate stacks.

Who This Databricks Migration Service Is For

This Databricks migration service is built for enterprises with meaningful investments in Hadoop, Snowflake, or legacy EDWs that need a structured way to move onto the Lakehouse. It’s a strong fit for teams under pressure to cut platform spend while improving analytics speed, reliability, and governance, enabling downstream functions to finally work from the same underlying data. If you’re not sure where to begin, a Databricks Migration Readiness Assessment helps you understand your current landscape and pinpoint the first workloads to move, then turns that into a realistic migration path tailored to your organization.

CTA: Take the Databricks Migration Readiness Assessment

What We Migrate to Databricks

Hadoop Migration to Databricks

Many organizations are still running critical pipelines on Hadoop clusters that have become fragile, expensive to operate, and increasingly difficult to staff as the ecosystem matures and skills move elsewhere. As workloads and data volumes grow, those clusters shift from being an asset to a bottleneck for both day‑to‑day reporting and newer initiatives such as AI and streaming analytics.

Tenjumps migrates Hadoop estates to Databricks by moving data from HDFS and related storage systems into cloud object storage backed by Delta Lake, then rebuilding ingestion and transformation flows on the Lakehouse in order to break that deadlock. All the existing Hive, MapReduce, and Spark jobs are translated into Databricks‑native patterns and workflows so that core logic is preserved while the platform gains in performance and governance and becomes far easier to operate over time.

Snowflake to Databricks Migration

Snowflake to Databricks is a good fit when you want BI and machine learning running on the same execution engine instead of split across platforms. It also suits teams that need tighter integration with Spark‑based data engineering or want to consolidate multiple warehouses and tools into a single Lakehouse with stronger, unified governance and lineage.

In these migrations, Tenjumps focuses on moving schemas and data efficiently while tuning compute usage and cost models for the Databricks environment. Role‑ and object‑level security is aligned with Unity Catalog, and workloads are replatformed so that queries, dashboards, and data products run reliably on Databricks SQL and your downstream BI tools.

Legacy EDWs and Other Platforms

Beyond Hadoop and Snowflake, many migrations involve long‑standing enterprise data warehouses such as Teradata, Oracle, SQL Server, and Netezza that are now at or near capacity and increasingly costly to extend. These systems often sit at the center of financial and operational reporting, so they are critical but difficult to adapt for new analytics and AI use cases.

Tenjumps migrates not only the data and schemas from these platforms but also the ETL and reporting logic that has built up over time. Stored procedures, views, ETL jobs, and embedded business rules are systematically extracted and reimplemented as Databricks workflows and dbt transformations, giving teams a modern, version‑controlled Lakehouse foundation without losing the business behaviors that reports and applications depend on.

Tenjumps Databricks Migration Factory Model

Tenjumps runs Databricks migrations through a factory‑style model rather than a series of one‑off projects. Predefined templates and reference designs set the structure, while automation does much of the heavy lifting on discovery and movement of data into Databricks. From there, Hadoop, Snowflake, and legacy EDW workloads follow a consistent playbook that is adapted to your specific platforms, tools, and compliance requirements.

This model is designed to shorten migrations and reduce risk. By cutting down on one‑off design decisions, it gives stakeholders clearer expectations about phases and deliverables and makes it easier to bring additional workloads into scope once the first wave is in motion. As more domains migrate, the same factory model—patterns, tools, and quality gates—can be reused so that risk and effort don’t grow in proportion to the number of systems you move.

Six-Stage Migration Framework

Assessment and Discovery

The work starts with a structured assessment that maps out your current platforms, workloads, and data flows, including how Hadoop, Snowflake, EDWs, and downstream BI tools depend on one another. From there, Tenjumps highlights high‑value candidates for early migration, focusing on lower‑risk areas first and calling out where deeper architectural changes will be needed.

Target Databricks Architecture Design

Next, the team defines the target Databricks Lakehouse: what the environment looks like, how Delta Lake and the Bronze/Silver/Gold layers fit together, and where Unity Catalog acts as the control point for permissions and lineage. After that, the focus shifts to how this design will actually be used, shaping networking, security, and integration around your priority analytics and AI use cases so that the platform mirrors real workloads. 

Data and Schema Migration

Data is then moved into cloud storage and converted into Delta tables, with schemas and objects from each source system mapped into Databricks. Every dataset is registered in Unity Catalog to establish ownership and access from day one, while automated checks confirm that volumes and key aggregates match expectations.

Pipeline and ETL Migration

Legacy ingestion and transformation logic is rebuilt using Databricks‑native services such as Auto Loader and Lakeflow Spark Declarative Pipelines, alongside dbt and Databricks workflows where they make sense. The aim is to preserve business behavior but replace brittle scripts and jobs with maintainable, observable pipelines that fit the Lakehouse model.

BI and Downstream Integration

Reports and dashboards are repointed or rebuilt on Databricks SQL and your BI tools, such as Power BI, Tableau, or Looker, using unified semantic layers and shared KPIs. Downstream applications and data products are then updated to read from the new Databricks‑backed sources so that existing business processes continue to run smoothly.

Testing, Parallel Run and Cutover

Before anything fully switches over, legacy and Databricks environments run side by side while outputs are reconciled and performance is benchmarked. Only after data quality and performance meet agreed thresholds does Tenjumps execute a controlled cutover with defined rollback options, aiming for zero data loss and high defect containment when production traffic moves.

How We Reduce Risk 

Preserving Business Logic and Data Quality

A successful Databricks migration depends on carrying forward the business logic and data that guarantees your reports and applications already rely on. Tenjumps uses a structured approach to extract stored procedures, views, ETL logic, and embedded business rules from your existing platforms. These are then rebuilt as DBT models, Databricks notebooks, or workflow jobs, with clear version control and documentation so that data pipelines stay transparent and maintainable.

This approach makes it easier for internal teams, and even auditors, to see how logic has moved and how behavior has changed. It also helps teams keep that logic evolving in line with changing business needs in a data‑driven way.

​To protect data quality, migrations include both automated and manual validation across legacy systems and the Databricks Lakehouse Platform. The process combines row‑level and aggregate checks with schema validation and targeted data quality tests (for example, using Great Expectations) so that critical tables and KPIs are confirmed before any production cutover.

Parallel Runs and Controlled Cutover

Before workloads fully switch to Databricks, Tenjumps runs legacy and Lakehouse pipelines side by side for an agreed period so that teams can reconcile differences and tune performance. During this parallel run, any discrepancies in results, as well as performance bottlenecks, are identified and resolved while the legacy system continues to handle production needs.

Cutover happens only once clear thresholds are met, with specific criteria defined for each workload or domain. The goal is zero data loss at cutover and a high level of defect containment so that users experience a smooth transition.

Working with Your Existing Teams

Databricks migrations are delivered in close collaboration with your teams so that design decisions and ownership boundaries are clear from the start. Tenjumps works jointly on architecture, governance, and operating models while ensuring your internal teams have a clear view of how reports are structured on Databricks.

Engagements can run in a few ways: advisory support, joint pods that blend Tenjumps engineers with your staff, or Tenjumps‑led delivery, with your teams focusing on business enablement. The same risk‑reduction principles also apply to specialized efforts such as BI report migration and production ML deployment.

Cost, Timeline, and Business Impact

Typical Migration Timelines

Most Databricks migration programs run between three and six months from initial assessment through cutover for the first wave of workloads, with duration driven by the number of source platforms and downstream dependencies in scope. The goal of the factory model is to keep this predictable by applying the same assessment, architecture, migration, and validation steps across Hadoop, Snowflake, and legacy EDW sources.​

Migrations are typically phased, starting with high-impact, lower-risk workloads that prove out the Databricks Lakehouse pattern before extending to additional domains. This phased rollout lets teams capture early wins and refine patterns before decommissioning larger portions of the legacy estate.​

Platform Cost Savings

A significant share of cost savings comes from consolidation, including Hadoop clusters, Snowflake instances, on‑premises EDWs, and overlapping BI platforms, into a single Databricks Lakehouse. As legacy infrastructure and licenses are retired and support contracts simplified, run costs come down and the overall surface area for governance and security gets smaller.

On the Databricks side, cost control comes from how the environment is run. Clusters are rightsized and allowed to auto‑scale, SQL warehouses are tuned to real workloads, and storage is organized with Delta Lake so that consumption tracks actual usage. 

Beyond Cost: Speed and Reliability

As workloads move to Databricks, teams typically see faster dashboard and query performance because SQL warehouses are optimized and pipelines feeding analytics are more streamlined. This improves the day‑to‑day experience for analysts and business users, who no longer have to wait through long refresh cycles or deal with inconsistent data loads.

Additionally, operational stability improves as standardized pipelines and unified governance cut down on surprise breakages and the manual fixes that follow. Modernized Databricks Lakehouse environments have allowed enterprises to move from multi‑hour dashboard refresh cycles to near-real‑time insights while lowering platform costs by consolidating legacy BI tools.

How to Start Your Databricks Migration

Databricks Migration Readiness Assessment

The Databricks Migration Readiness Assessment is designed for organizations that are weighing a move off Hadoop, Snowflake, or other legacy data platforms and want to understand what that shift would look like. It’s the right fit when you need a structured view of where Databricks belongs in your architecture, which workloads to move first, and how to sequence change in a way that doesn’t disrupt critical reporting and operations.

Next Steps After the Assessment

Many teams follow the assessment with a quick‑win migration pilot, using a focused proof of concept on a specific warehouse area or a defined group of critical reports to validate the Databricks approach and show concrete value. These pilots are scoped with clear success criteria so that you can see whether performance or cost actually improves before you commit to additional domains.

From there, you can expand into a full Databricks migration program, engaging Tenjumps pods to run the end‑to‑end work using the factory model for assessment, architecture, data and schema migration, pipelines, BI, and cutover. Throughout, your internal data, BI, and platform teams stay closely involved so that ownership and knowledge transfer are built into the delivery process.

Get your Databricks Migration Readiness Assessment to see what a move to a Databricks Lakehouse could look like for your organization. 

If you prefer to start with a conversation, you can talk to a Databricks architect about your current environment and potential migration paths for your most important workloads.