Most enterprises reach a point where the BI layer slows decisions more than the data platform underneath it. Over time, the mix of reports and ad hoc dashboards, plus SQL logic buried in too many places, turns every metric into a debate and makes even small changes feel as if they will break something important.

Tenjumps uses Databricks as the backbone for BI migration and runs the work like an engineered factory. The goal is to replace a fragile, tool-bound reporting estate with a governed system on your core data platform that can keep pace with the business. When the work is done, you have fewer reports people rely on and faster ways to adjust them, and your BI environment is ready for the analytics and AI projects you actually want to ship.

When to use a BI migration factory

The problem with BI sprawls

Before you can evaluate a BI migration factory, you need a clear picture of the problem with traditional BI sprawls. In most organizations, BI grew one request at a time. Old platforms sit alongside newer tools, and the result is a tangle of legacy reports that overlap, contradict each other, or depend on slightly different slices of the same data. Over time, SQL and business logic get scattered across multiple surfaces, so nobody can see the full path from source to metric. Maintenance turns into firefighting, and each schema change or new initiative feels as if it will knock something critical offline.

As that pattern repeats, leadership starts to doubt the numbers. In response, teams fall back to relying on screenshots and offline extracts because official reports feel slow to change or prone to breaking under load. From there, new projects often add yet another layer of reporting instead of simplifying what exists, and the BI estate becomes harder to govern and increasingly unrealistic to modernize without a structural rethink.

When to call Tenjumps

For most teams, there is a clear point where BI starts getting in the way of how the business runs: The current setup obviously will not carry you through the next wave of growth, but you cannot stop operations to rebuild it. You see it when a planned tool consolidation or move to Databricks drags on for months because nobody wants to simply recreate the reporting chaos in a new place. That is when you call Tenjumps.

The trigger is cultural as much as technical. KPI logic lives in reports and stored procedures instead of in a governed layer, so every leadership review turns into a debate over whose metric is right. As that pattern repeats, any attempt to standardize across domains drags on and burns time. A BI migration factory engagement will pull that logic into a shared semantic foundation on Databricks, so tools can change without reopening the same arguments every quarter.

Why a factory model beats one-off migrations

One-off report rebuilds tend to recreate the same problems on new tooling. Each team solves for its own backlog, patterns drift, and technical debt snaps back as soon as pressure returns. The surface looks different, yet the underlying operational load and risk profile stay roughly the same.

By contrast, a factory model assumes from the start that BI migration will touch multiple domains over time. Tenjumps runs an end-to-end flow—discovery, rationalization, transformation, validation, and cutover—that stays consistent as you move from finance to operations to customer analytics. Because automation and repeatable playbooks handle much of the heavy lifting, the work stops depending on heroics and starts behaving like a durable part of your core data platform strategy.

Tenjumps’ five-step BI migration factory model

Step 1: Inventory and dependency mapping

Every serious BI migration starts with an honest inventory. In Step 1, Tenjumps builds a complete picture of your current estate by scanning BI platforms and core data sources to capture reports, dashboards, datasets, and the SQL or ETL logic behind them. That includes older environments like SSRS, Cognos, SAP‑based reporting, and on‑prem Power BI, alongside newer tools and the warehouses or operational systems they query.

Once that baseline exists, we trace dependencies to see which BI assets share tables, where KPIs diverge, and how much business logic is embedded in report‑level calculations or stored procedures. This rationalization separates what is critical from what is noise and highlights where consolidation or retirement is safe. By the end of Step 1, you have a fact‑based view of the BI environment you are about to move, which reduces guesswork and lowers the risk of breaking something important later in the migration.

Step 2: Rationalization and target design on Databricks

Next, we design a target state with Databricks at the center of your BI platform. In Step 2, Tenjumps groups related reports into domains such as finance or customer so that the target model reflects how the business actually operates, not how individual tools happened to evolve.

From there, we define the architectural pieces: which workloads run in Databricks SQL, which datasets are promoted into curated Delta tables, and how the semantic layer presents reusable measures and dimensions to downstream tools. The migration approach is aligned with your data governance standards, so metadata, access policies, and lineage expectations are wired in from the start. The result is a practical blueprint that ties your data platform, BI tools, and day‑to‑day workflows into a governed system instead of another round of scattered projects.

Step 3: Automated SQL and ETL migration

With the target design in place, Step 3 is about moving real work, not drawing more diagrams. Tenjumps uses migration accelerators to pull logic out of reports and into Databricks, translating legacy SQL patterns, embedded calculations, and existing ETL jobs into Databricks‑native SQL, workflows, and pipelines that run on top of your medallion‑style datasets. We refactor the old setup’s fragile report queries into reusable views that can support more than one migrated report without constant rework.

This is also where automation pays off. Instead of hand‑coding every change, we lean on repeatable patterns and templates to reduce manual effort and keep migration costs under control. The aim for this step is a clean, testable state in Databricks that can handle today’s reporting needs and leaves room for the BI modernization work you already have queued up.

Step 4: Validation, optimization, and performance tuning

No migrated report is treated as complete until it clears testing. In Step 4, Tenjumps runs side‑by‑side checks across each family of BI reports, comparing outputs from the legacy and new environments to confirm that row counts, aggregates, filters, and KPI behavior match expectations. Automated checks surface discrepancies early, which lets the team correct issues before business users depend on the new setup.

At the same time, we tune for performance and cost. That work ranges from adjusting Delta table layout and partitioning to shaping how compute is used for interactive dashboards and scheduled refreshes. Where front‑end design is adding unnecessary load, we simplify the way results are presented so that people get answers faster. The outcome is a BI environment where migrated reports hold up under real usage instead of only passing controlled tests.

Step 5: Cutover, adoption, and scale-out

The last step is about turning a successful pilot into how BI runs in production. Tenjumps sets timelines and a staged cutover plan by domain so that teams know when specific reports will move and how to reach them afterward. Each migrated report is mapped back to its legacy version, which gives business users an easy way to confirm they are looking at the right output during and after the transition.

After those first domains are stable, the same migration flow is applied to additional BI systems, tools, and regions. Tenjumps leaves behind templates, documentation, and working examples so that internal teams can keep using the factory model without waiting on another project. Over time, that pattern produces a streamlined BI environment on Databricks, with a governed semantic layer and standardized data models built to support current reporting and the AI work that follows.

What you get from BI migration automation

A governed, reusable semantic layer on Databricks

After migration, report logic sits in shared Delta tables and views, exposed through semantic definitions that any BI tool can tap. From there, Unity Catalog controls who sees what and how data moves across domains, so finance, operations, and customer teams work from the same governed model instead of running their own versions on the side.

Fewer reports, stronger KPIs

The factory is built to consolidate reporting, not recreate every legacy report one‑for‑one. Over time, you end up with a smaller, more focused set of dashboards anchored in standard metrics and curated outputs that leadership can rely on without side debates over the numbers. Because KPI definitions live in one place, changing them becomes a controlled update instead of a negotiation across dozens of teams.

Automated, observable pipelines

ETL that used to hide inside reports or fragile jobs is replaced by transparent Databricks pipelines with real monitoring and alerting. As a result, data engineering takes clear ownership of reliability, and schema changes or new sources are handled through defined workflows, which cuts down on surprise breakage when upstream systems move.

BI that is ready for AI

With reporting built on curated Gold tables and a shared semantic layer, the same foundation that feeds BI also supports AI and GenAI workloads. That alignment is core to how Tenjumps and Databricks work together: a governed data stack that serves daily reporting needs and the more advanced intelligence you are planning, without having to spin up a separate platform just for experiments.

How Tenjumps pods deliver BI migration

Cross-Functional pods focused on BI outcomes

For BI migration, Tenjumps builds pods that combine data engineers, BI specialists, and platform talent into one accountable unit. Each pod owns throughput and quality, tracking migrated‑report counts and validation coverage so that progress and risk stay visible in day‑to‑day reviews. Over time, post‑cutover defects are treated as a metric the team is responsible for driving down.

Factory-style accelerators and patterns

From there, these pods work from a shared set of accelerators instead of reinventing the approach for each domain. Discovery templates and SQL translation libraries set the baseline, while validation harnesses and cutover playbooks keep the work moving in a consistent rhythm across business areas. Together, those patterns turn BI migration into a system your organization can rely on more than once.

Partnering with your internal teams

Tenjumps also leans on people who know your reporting best. Tenjumps Pods work alongside internal BI, data, and business stakeholders to prioritize reports, approve consolidation decisions, and settle KPI definitions that leadership will stand behind. As the factory runs, your teams get documentation, training on the target models, and support to take over day‑to‑day operation, so BI migration work wraps up cleanly and the resulting BI environment becomes a stable part of how you run the platform.

Engagement structure–from assessment to scaled migration

Phase 1: BI migration assessment

The engagement starts with a focused assessment of a representative slice of your BI estate. Tenjumps looks at a small set of domains, records the reports that matter, and traces how data and SQL flow into those outputs. You leave this phase with a clear catalog of those priority reports and a mapped view of key dependencies, plus a Databricks‑based BI design that makes the scope and opportunity of the migration explicit.

Phase 2: Pilot domain migration

Next, Tenjumps runs the BI migration factory model against one well‑defined domain such as financial reporting or customer analytics. The team runs that domain through the full factory flow so that you can see the impact in live reporting. You get cleaner outputs and a noticeable lift in performance and governance in an area the business feels every day. Lessons from this pilot sharpen the patterns and playbooks before you commit to broader rollout.

Phase 3: Scale‑out across reports, tools, and domains

Once the pilot is stable, the same factory model is extended to additional report families, regions, and business units. New BI tools and workloads are pulled into this structure over time, while logic stays centralized in Databricks and governed through Unity Catalog so that complexity does not creep back in.

FAQ

How does migration differ from rebuilding reports in a new BI tool?
Rebuilding reports in a different BI tool usually recreates the same sprawl on a new surface. Tenjumps focuses on the underlying structure instead, moving business logic and SQL into Databricks so that reports draw from a governed semantic layer, resulting in a stable reporting foundation that outlasts any single BI tool.

Can you handle multiple BI platforms and databases at once?
Yes. The migration factory is designed to work across mixed estates that might include Power BI, Tableau, Qlik, SSRS, and other legacy platforms pointing at various warehouses and operational systems. Tenjumps pods map dependencies across these platforms up front, so consolidation into Databricks is coordinated rather than handled one source at a time.

What happens to our existing ETL, stored procedures, and custom SQL?
Where those assets still matter, they are treated as inputs to the migration. Tenjumps extracts and analyzes the logic, then refactors it into Databricks workflows, SQL, and data models that sit behind your reports. That approach removes hidden ETL from tools and stored procedures while preserving the behavior the business relies on.

How do you ensure that metrics and KPIs stay consistent after migration?
During migration, KPI definitions are centralized into shared views and semantic definitions on Databricks. Tenjumps uses side‑by‑side comparisons between legacy and target outputs and tracks discrepancies until they are resolved. Once the semantic layer is in place, changes to a metric happen only once, at the core.

How long does a typical BI migration pilot take, and what resources do we need on our side?
Most pilots focus on a single domain and run as a contained project, with timing driven by the number of reports, data sources, and integrations involved. On your side, you typically need a product owner for the domain, representation from BI and data engineering, and someone who understands current reporting usage. Tenjumps pods handle the migration workload while your team validates behavior and signs off on consolidation decisions.

Can this approach support near‑real‑time reporting and dashboards?
Yes. Because reporting is moved onto Databricks pipelines and curated datasets, the same patterns that support batch refresh can also support streaming or frequent updates where needed. Tenjumps designs these flows so that near‑real‑time use cases use appropriate pipelines and compute without degrading more-traditional reporting workloads.

How do you think about performance and cost for BI queries on Databricks?
Performance and cost are part of the migration design, not a post‑go‑live concern. Tenjumps tunes table layout, caching, and compute choices to match usage patterns for BI queries and dashboards. As part of the factory model, these patterns are reused across domains so that you get predictable behavior and a clear handle on how query workloads translate into spend.

What if our team is new to Databricks and Spark?
The migration is structured so that your team can learn the platform while seeing real outcomes. Tenjumps pods handle the heavy lifting at first, with shared patterns, documentation, and examples that your engineers can adopt over time. By the end of the engagement, teams are working with familiar templates and semantic models.