Move from fragmented, high-maintenance data platforms to a governed Databricks Lakehouse where analytics and AI run on the same trusted foundation. 

• Consolidate scattered data and pipelines so that teams work from a single, trusted view of the business.

• Run migrations and new builds through a repeatable approach that turns complex estates into predictable Databricks programs with clear milestones and ownership.

• Improve platform economics and reliability with architectures and operating models built to lower run costs, shorten delivery cycles, and keep production issues contained.

Get your Databricks Readiness Assessment to understand where Databricks fits in your stack and what a realistic migration roadmap looks like for your organization. 

Why Modernize: From Legacy Platforms to a Databricks Lakehouse

Common Legacy Patterns Holding Teams Back

Most enterprises reach Databricks after years of stretching traditional data platforms past their limits. The warehouse is being held together by ad hoc fixes and one‑off logic, and because dependency chains are fragile, any modification can cause unintended side effects elsewhere.

Hadoop clusters and batch-heavy ETL struggle with today’s streaming and near-real-time expectations, which means even small updates can turn into slow, painstaking efforts. At the same time, different teams rely on different BI tools and datasets, so they spend more time debating whose numbers are right than deciding what to do with the insights they have.

In day-to-day operations, that reality looks like long refresh windows, dashboards that never quite match what the business is seeing on the ground, and governance processes that run on spreadsheets and manual spot checks. Against that backdrop, AI projects often look impressive in pilot form, but without consistent, governed data that cuts across departments, they struggle to grow into the kind of production systems leadership can depend on.

What a Lakehouse and Data Intelligence Platform Can Do for Your Business

A Databricks Lakehouse replaces a stack of disconnected systems with one governed environment where storage, compute, and governance work in sync. 

Key shifts include:​

• Clear data layers. Data lands in medallion layers—Bronze for raw ingestion, Silver for refined datasets, Gold for business-ready tables—so teams know exactly which layer to use for everything from exploration to downstream applications.​

• Centralized governance. Unity Catalog handles permissions, cataloging, and lineage centrally, instead of scattering those responsibilities across tools and teams. That gives you a single view of who can access which data and how it is being used across the organization.

• One source for analytics and AI. BI, self‑serve analytics, and machine learning work from the same curated datasets rather than from separate, partially overlapping copies. As a result, duplicate logic and conflicting versions of the truth start to disappear.

For a business, this means faster, more reliable analytics and a platform where data science teams can experiment and deploy without creating new silos or compliance issues. It also means stakeholders get clearer visibility into how data moves and changes over time, making regulatory reviews more predictable and less resource‑intensive. 

If timing is the question, a Lakehouse Readiness Assessment helps you map your current environment and shape a Databricks roadmap tied directly to revenue and risk.​

Tenjumps and Databricks

A growing number of enterprises now rely on the Databricks Data Intelligence Platform for their most important analytics and AI workloads, because it combines the scale of a data lake with the reliability of an enterprise data warehouse in a single environment. Rather than stitching together separate systems for ingestion, warehousing, BI, and machine learning, they consolidate on a Databricks Lakehouse to cut data sprawl, tighten governance, and give teams a shared foundation that can support real production use. 

Tenjumps is a premier Databricks partner with global engineering teams that build and run Lakehouse platforms for large enterprises. The work centers on turning Databricks into a production-grade environment with clean medallion architecture, reliable pipelines, governed access, and live ML workloads, so that day-to-day reporting and AI both run on the same trusted system at scale.

Key Databricks Service Areas

Databricks Migration Services

When you move to Databricks with Tenjumps, the work covers full estates—Hadoop clusters, Snowflake environments, and on‑premises warehouses such as Teradata, Oracle, and SQL Server—and brings them into a single Databricks Lakehouse that can support day‑to‑day analytics and AI. Data and schemas move to Databricks along with the business logic that lives in stored procedures, views, and ETL jobs, so reports and applications keep behaving the way your teams expect, only on a more modern platform.

The migration runs through a standardized, factory-style model rather than a collection of one-off scripts. That model starts with a detailed assessment of platforms and dependencies, then moves through target architecture design, data and schema migration, pipeline rebuilds, BI integration, and a controlled cutover. At each stage, automation and repeatable patterns keep work predictable, while parallel validation compares legacy outputs with Databricks results so that issues are surfaced and resolved before production traffic moves.

We ensure that for your teams, a Databricks migration feels structured instead of experimental. They’ll see clear milestones and understand when and how validation will happen. And as legacy platforms are consolidated onto a single Lakehouse, leadership can be confident that risk is managed—the goal is zero data loss and tight defect containment at cutover.

Lakehouse Implementation and Architecture Design

Some teams come to Databricks without a legacy migration in play. Others already have Databricks running, but the environment grew organically and now feels hard to govern or extend. In both cases, the first job is to get the Lakehouse architecture right so that it can support long‑term data and AI work, even as new use cases and data sources are added over time.

Tenjumps designs Databricks Lakehouse environments with explicit medallion layers, a clear Delta Lake strategy, and Unity Catalog at the center of governance. That means defining how data lands in Bronze, how it is refined into Silver, and what qualifies as a Gold, business-ready table, then backing those decisions with standards for naming, quality, and lineage. 

This path is a good fit when you see Databricks as the central data and AI platform in your technology stack. It gives you a repeatable landing zone where new analytics projects and use cases can all plug into the same structure instead of you having to create fresh silos each time. Over time, that consistency allows for faster onboarding of new data sources and a platform that can absorb more demand without becoming chaotic.

BI Modernization and Report Migration

When your BI stack includes tools such as Cognos, SSRS, OBIEE, Tableau, and Power BI, keeping metrics aligned and performance predictable becomes a challenge. Tenjumps helps by consolidating and migrating each tool’s reports onto Databricks-backed analytics, then rebuilding them on top of unified semantic layers and a consistent set of KPIs so that different dashboards finally tell the same story. Databricks AI/BI Genie allows business stakeholders to interact with the platform using natural language to easily generate reports and insights.

Behind the scenes, an automated migration approach handles much of the report and SQL conversion, while structured validation ensures that numbers match what business users see today. Performance tuning on Databricks SQL and your chosen BI tools replaces fragile, one-off lift‑and‑shift efforts, so teams experience faster dashboards and a BI footprint that is easier to govern and evolve over time.

Data Governance and Security on Databricks

On Databricks, governance works best when it is part of the architecture. Tenjumps leans on Unity Catalog as the center of that model, using it to define fine‑grained access control, maintain a searchable catalog of data assets, and track lineage and audit trails across workspaces and data products. That gives security, data, and compliance teams a single place to understand who can see what, how data is moving, and where sensitive fields show up.

Security controls and PII handling are then aligned with the standards your organization cares about most, such as SOC 2, PCI‑DSS, GDPR, or ISO frameworks. Instead of slowing projects down, these patterns turn governed access into a baseline that analytics and AI projects can rely on: Permissions are clear, data classifications are visible, and audit evidence is built into the way the platform operates. The result is a Databricks environment where risk and compliance teams feel in control while data and AI teams still have room to move quickly.

AI, MLOps, and Real-Time Analytics

On the AI side, Tenjumps focuses on building production-grade machine learning. Teams use Lakeflow and Feature Store to move from ad hoc notebooks into repeatable pipelines. Databricks-native workflows then manage training and deployment as part of a standard lifecycle. This gives data science and engineering teams a consistent way to promote models, track versions, and roll back safely when behavior drifts. Over time, the AI layer can support multiple use cases without collapsing into a tangle of isolated projects.

For real-time and near-real-time analytics, the focus shifts to streaming patterns that keep operational data current. Tenjumps uses Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) to define how events are ingested and processed as they arrive. Delta Lake provides durable storage and reliable schema handling so those event streams stay trustworthy. That combination can power event-driven scenarios such as anomaly detection or logistics tracking. It also feeds low-latency dashboards that frontline teams rely on throughout the day. As more streams come online, the same patterns and governance controls are reused so that real-time capabilities expand without sacrificing reliability.

Cost Optimization and Performance Tuning

We treat cost optimization as an ongoing discipline. Once the initial Lakehouse and workloads are in place, Tenjumps continues to adjust the Databricks environment so it reflects how teams actually use data over time.

Clusters are configured with workload-aware strategies and right-sizing policies, enabling compute to scale up when needed and pull back when demand falls. Storage layouts and caching approaches are refined to keep frequently accessed data fast without driving unnecessary costs. Performance and quality monitoring run in the background, which helps catch regressions early and keeps defect rates in check as data volumes grow and new applications come online. The aim is a platform that stays efficient and predictable as it scales.

How Tenjumps Delivers Databricks Programs

Migration and Delivery Factory Model

Tenjumps uses a factory-style delivery model that relies on standardized templates and automation to keep work consistent from project to project. That same model applies whether the team is migrating data, building out a Lakehouse, modernizing BI, tightening governance, or supporting ML workloads.

Instead of treating each initiative as a one-off, our teams move through a defined six-stage approach that starts with assessment and target architecture and then progresses through migration and pipeline rebuilds. BI integration and a parallel run phase come later so that changes can be validated before full cutover.

This way of working tends to shorten migration and modernization timelines while keeping risk in check. Because quality gates are built into each stage, programs can aim for outcomes such as zero data loss at cutover and strong defect containment across both pipelines and reports.

Global Delivery and Pod-Based Teams

Delivery is handled by dedicated pods, which are cross-functional teams that own concrete outcomes on your Databricks platform instead of completing isolated tickets. Each pod typically brings together Databricks-certified technical leadership, data engineers, BI specialists, QA, DevOps, and a scrum master; it can also draw on agentic AI to speed up development and operations where it adds real value.

This structure makes pricing more predictable and creates clear ownership for platform and business KPIs. It also supports multiple engagement models, whether the need is architectural advisory work, build teams for new workloads, managed services for day-to-day operations, or value realization pods that concentrate on measurable business impact.

Throughout this guide, case studies highlight how pods have taken on large-scale migrations and BI consolidation for complex enterprises. We’ve used the same model to run ongoing Databricks operations in sectors as diverse as manufacturing, financial services, retail, and logistics.

Cost Advantage and Quality Controls

Tenjumps combines India-based delivery centers with onshore leadership to keep delivery efficient while still enforcing consistent quality standards. This mix supports scale without turning every project into a hard-to-govern bespoke effort. Automation is embedded into the way work gets done, not treated as an afterthought. Standardized playbooks and continuous improvement practices cut down manual effort in testing, validation, monitoring, and incident response, which helps control platform costs and keeps release cycles moving even as new workloads appear.

​Taken together, these patterns create a modernized Databricks environment that is run with stable performance and governance over time. As usage grows, the platform is less likely to spike in cost or risk, because operational discipline scales alongside adoption.

Industry Applications and Use Cases

Common Cross-Industry Patterns

Across industries, organizations use a Databricks Lakehouse to create a single, governed source of truth for finance, operations, and customer data. Business users gain self-service analytics on curated, trusted datasets, while data science teams build AI use cases, such as forecasting, anomaly detection, optimization, and personalization, on the same shared foundation.

As more workloads move onto the Lakehouse, governance and lineage become easier to manage at scale, which is critical for regulated industries and any organization that needs clear audit trails across financial and operational data.​

Industry Snapshots

Manufacturing

Manufacturing teams use Databricks to keep plants running smoothly and to expose the sources of quality problems. A Lakehouse built from sensor readings and production data gives engineers a live view of equipment health. This reduces unplanned downtime and makes materials and capacity planning more predictable.

Financial Services

In financial services, Databricks sits underneath fraud detection, risk modeling, and regulatory reporting as a governed data foundation. Transaction and behavior data share the same environment as curated reference data, which lets risk and compliance teams see the full picture. Institutions can cut fraud losses and sharpen capital and liquidity insights while maintaining strict control over permissions and lineage.

Retail and E‑Commerce

Retail and e‑commerce organizations turn to Databricks when they want a clearer picture of demand and customer behavior. Product and inventory data live alongside behavioral signals in the Lakehouse, so merchandisers and marketers can react to what is actually happening in the market. This leads to more accurate forecasts and fewer painful stockouts or overstocks across channels.

Logistics and Supply Chain

Logistics and supply chain teams rely on Databricks for a real-time view of how goods move through their networks. Order and warehouse data are consolidated on the Lakehouse, which makes route optimization and network monitoring far more responsive to changing demand, delays, and disruptions. As a result, businesses can lower transportation costs and respond faster when disruptions threaten on‑time delivery.

Results You Can Expect

Tenjumps Databricks programs are built to move the needle on everyday operations. Clients see faster dashboards and analytics, lower platform and license spend through consolidation, quicker paths from model development to production, and stronger governance that makes audits and reviews less disruptive.​

Examples from Recent Engagements

Financial Services

A leading financial services firm’s Human Capital Management group needed a faster way to re‑engage high‑value candidates left idle in its applicant tracking system during a hiring freeze. When hiring resumed, 70–80 qualified candidates were still in the ATS, but recruiters had no efficient way to see who was available, relying instead on manual LinkedIn searches that took weeks and still missed top profiles.

Tenjumps designed and deployed a phase‑one Agentic AI MVP in just 10 days, using only three data points—name, email, and previous employer—to automate the heavy lifting. Resumes from the ATS were ingested across formats, a custom matching algorithm verified employment status with around 90% confidence, and AI scored candidates on availability, stability, and fit, surfacing a prioritized, real‑time view in a dashboard. The solution re‑engaged more than 70 qualified candidates directly from the ATS, cut verification time from weeks to minutes, slashed recruiter labor costs by roughly 99%, and freed recruiters to focus on conversations instead of status checks.

Logistics and Retail Operations

A global logistics provider and e‑commerce parcel specialist found its customer service team overwhelmed by email volumes, with 14,145 messages over a three‑month period—an average of 155 per day, 83% of them related to shipping status or missing packages. Manual triage meant CSRs spent most of their time answering routine tracking questions, slowing responses on urgent issues and making it hard to scale as volumes grew.

Tenjumps designed and deployed an AI‑powered chatbot that became the first line of response for shipping‑related inquiries within two months. The bot now instantly handles more than 60% of daily tickets, prompting customers for missing details upfront and providing 24/7 tracking updates across 200+ countries and territories. That shift freed CSRs to focus on higher‑value, complex cases while creating a more scalable support model that protects customer loyalty and repeat business.

You can explore more Databricks-focused stories and detailed outcome metrics in the case study library.

How to Get Started

Choose the Right Place to Begin

Lakehouse Readiness Assessment

For organizations that know their current stack is under strain but need a clear plan, the Lakehouse Readiness Assessment provides a focused review of your data and analytics environment, including key platforms, workloads, and constraints. In one to two weeks, you receive an ROI-backed roadmap that highlights the most impactful workloads to move to Databricks first, outlines architecture options, and shows how migration and modernization would align with your business goals.​

Quick-Win Pilots

If you want to prove value before committing to a broader program, you can start with a contained pilot such as a data warehouse migration POC or a BI dashboard modernization project. These pilots are designed with clear timelines and success criteria so you can see concrete improvements in performance or time-to-insight on Databricks before scaling to additional workloads.​

Full Databricks Migration and Modernization Program

When you are ready to move beyond pilots, Tenjumps runs end-to-end Databricks programs that cover assessment, migration, Lakehouse architecture, BI, governance, and ML using pod-based delivery. This option is ideal for enterprises that want a single accountable partner to design, build, and operate a Databricks Lakehouse that supports day-to-day reporting and AI at scale.​

Take the Next Step

Get your Databricks Readiness Assessment to understand where Databricks fits into your roadmap and what modernization could deliver over the next 6–12 months. 

If you prefer to talk through options first, you can schedule time with a Databricks-certified architect or download the Migration and Modernization Playbook to review the approach in more detail with your team.​

Databricks FAQs

Q: Why choose Tenjumps and Databricks instead of extending our current warehouse or Hadoop cluster?

A: Legacy warehouses and Hadoop environments were not built for today’s mix of streaming data, complex data, and AI‑driven workloads, so every new project becomes slower and more expensive to deliver. Tenjumps pairs a unified Databricks Lakehouse and Data Intelligence Platform with a modern data architecture and delivery engine. This turns the platform into working production systems instead of another layer on top of brittle infrastructure, enabling teams to move toward more data‑driven decision‑making.

Q: How do Tenjumps and Databricks complement each other?

A: Databricks provides the scalable Lakehouse foundation, Delta Lake, governance layer, and core data management and data processing capabilities, while Tenjumps builds and runs the data pipelines, medallion architectures, dashboards, ML workloads, and automation on top of it. Together, the partnership gives enterprises both the right platform and the engineering muscle and operating model required to ship and sustain production data and AI systems tailored to real business needs.

Q: How quickly can we expect to see value in production?

A: Most joint Tenjumps-Databricks programs deliver initial production workloads in roughly three to six months, depending on scope and data analytics complexity. The migration and modernization factory model, along with reusable assets for ingestion, Lakeflow, and governance, is designed so that quick‑win pilots can land early while the groundwork is laid for a broader Lakehouse rollout that streamlines data integration and prepares future machine learning models.

Q: How do Tenjumps and Databricks help control platform and cloud costs?

A: The partnership centers on consolidation and efficiency. Moving from scattered warehouses, aging Hadoop clusters, and overlapping BI platforms onto a single Databricks Lakehouse and business intelligence ecosystem cuts both infrastructure and license costs while simplifying the data stack. From there, Tenjumps focuses on how work actually runs. The goal is to ensure clusters are rightsized and set to auto‑scale, SQL warehouses are tuned, and engineering practices are standardized so that ongoing consumption tracks real usage while keeping data processing and analytics streamlined.

Q: What about security, governance, and compliance on the combined platform?

A: On the platform side, Databricks provides centralized governance through Unity Catalog, along with fine‑grained access control and end‑to‑end lineage across workspaces, data products, and data pipelines. Tenjumps turns those features into day‑to‑day practice by defining permission models and enforcing data management and data quality checks in line with standards such as SOC 2, PCI‑DSS, ISO frameworks, and GDPR. This gives risk and compliance teams a clearer, more auditable environment than the legacy stack ever delivered, without slowing down data‑driven teams.

Q: Can Tenjumps work alongside our existing data and engineering teams while we adopt Databricks?

A: Yes. Joint programs typically use cross‑functional pods that include Tenjumps engineers and your internal stakeholders, combining Databricks expertise with your domain and system knowledge to create a unified ecosystem. Engagements can start as advisory, then move into joint delivery or become managed services over time, with shared standards and documentation so that your teams can take on more responsibility without sacrificing stability or the ability to run and evolve machine learning models.

Q: What does success look like 6–12 months after starting with Tenjumps and Databricks?

A: Successful customers typically reach a point where their medallion architecture on the Databricks Lakehouse is clean and consistent, with data architecture and data pipelines aligned to how the business actually operates. As that foundation settles, key domains and reports move onto the platform, and the first AI or ML use cases begin running in production through pipelines managed with Lakeflow and supported by advanced analytics and business intelligence.

At that stage, leaders start to notice that analytics arrive faster, are easier to trust, and better inform decision‑making. As tools and infrastructure are consolidated, platform and license costs drop. With that in place, an operating model built around pods, governance, and automation can absorb new workloads and support more AI‑powered and AI‑driven use cases over time.

Q: How does this partnership support AI and GenAI initiatives specifically?

A: Databricks supplies the core data intelligence layer, elastic compute, and data integration capabilities that AI workloads depend on. Teams can use these to support both traditional ML and GenAI, including use cases such as vector search and retrieval‑augmented generation on enterprise data, backed by clean data pipelines and data processing. Tenjumps then focuses on what sits underneath those use cases. Data foundations, feature stores, and ML pipelines are put in place so models train on clean, well‑tracked complex data. With governance wrapped around that stack, those models are deployed into real business workflows, making AI initiatives more reliably data‑driven.

Q: What if we’ve already started on Databricks but the environment isn’t delivering the value we expected?

A: In that case, Tenjumps concentrates on stabilizing and reshaping the existing Databricks environment. Medallion layers are clarified so data flows are easier to understand, and Unity Catalog governance is tightened to match how teams actually need to work with sensitive data, improving both data management and data architecture. Tenjumps then reworks pipelines to reflect real usage patterns instead of legacy assumptions and better support advanced analytics and machine learning models. The aim is to turn a partially adopted or underutilized Databricks footprint into a dependable Lakehouse foundation that can streamline analytics delivery and support more AI‑powered workloads.