Databricks
LTTS partners with Databricks to help engineering-intensive enterprises modernize legacy data estates, unify IT and operational technology (OT) data on the Databricks Lakehouse, govern it with Unity Catalog, and take analytics, machine learning and generative AI into production. The alliance pairs the Databricks Data Intelligence Platform with LTTS’ domain engineering expertise across manufacturing, mobility, medtech, semiconductors and energy.
Most data and AI programs stall on the data, not the platform: sensor feeds without context, plant systems that don’t talk to ERP, and quality rules nobody owns. LTTS engineers have spent decades inside those products and plants. That depth lets us design data models, pipelines and AI use cases that reflect how your equipment, processes and supply chains actually work.
Our Databricks practice covers the full lifecycle, from assessment and migration through data engineering, governance, AI and managed operations. We deploy on AWS, Microsoft Azure and Google Cloud, and bring ready-made accelerators so clients move from discovery to proof of concept to enterprise scale in weeks, not quarters.
Why Enterprises are Moving to the Lakehouse Now
Cost and Technical Debt Are Crowding Out Innovation
System and application maintenance consumes most of the IT budget, and technical debt builds up quickly on proprietary platforms. Meanwhile, business processes change faster than IT can keep up. An open Lakehouse lowers run costs and gives every team one platform to build on.
AI Is Only as Good as the Data Behind It
A model is only as reliable as the data it consumes. Incorrect or incomplete data amplifies hallucinations, and the cost of a wrong prediction on a production line or in a clinical setting is far higher than the cost of an AI project that never launched. Trusted, governed data has to come first.
The Real Value Lies in Curated Engineering Data
Engineering and industrial data holds enormous potential, but it is rarely ready for traditional BI. Rapid advances in AI have widened what is possible with OT data, and curating OT and IT data together is now the most direct route to that value.
Why choose LTTS as your Databricks partner:
Faster Time to Value
- Discovery to scale in weeks
- Legacy ETL to governed Lakehouse
- AI accelerates the software lifecycle
Engineering Depth
- Domain engineering meets Lakehouse and AI
- Productized use cases, architectures, accelerators
- Industrial data know-how complements Databricks
Proven Delivery
- Nine marquee accounts across three regions
- Anchor engagement within L&T group
- Methodology built for on-time, on-budget delivery
Open Standards
- Open standards enable out-of-the-box integrations
- Manufacturing, product and plant engineering expertise
- Integrations that reach the shop floor
Joint Collaboration
- One platform to store, curate, analyze, agentify
- Large client base with engineering depth
- Immersive labs and joint sandbox demos
Context, Semantics and Ontology
- Distinct layers with independent ownership
- AI agents interpret engineering data correctly
- Layers evolve without breaking others
LTTS Accelerators and IP for Databricks
The following assets plug in from day one:
What it does
Deploy and orchestrate reusable AI agents across business and engineering workflows.
Impact
Up to 50% faster AI solution deployment and improved workforce productivity.
What it does
Automates data quality validation, monitoring and remediation.
Impact
Up to 60% fewer data quality issues and more reliable AI outcomes.
What it does
Unifies metadata, lineage, governance and data product management.
Impact
Up to 40% faster governance rollout and compliance readiness.
What it does
Accelerates ingestion and integration of OT, IT and enterprise data sources.
Impact
30-50% lower integration effort and faster onboarding of new data sources.
What it does
AI-assisted engineering and delivery framework that accelerates solution development, testing and deployment.
Impact
Up to 30-40% faster project delivery with improved productivity and reduced delivery risk.
Databricks Reference Architecture
Our reference architecture takes data from any source to any consumer on one unified, open and scalable Lakehouse, orchestrated end to end with Lakeflow Jobs (schedules, triggers, retries, alerts and monitoring).
- Sources: Files and logs (semi-structured), databases and ERP or business applications (structured), media and streaming (unstructured)
- Ingest: Batch ingestion with Auto Loader, stream ingestion with Azure Event Hubs or Kafka, and change data capture with HVR or Fivetran
- Transform: Medallion ETL from raw integration to filtered, cleaned and transformed data, to business-ready tables
- Query and serve: Data warehouse and data engineering workloads on Databricks SQL, plus a RAG pipeline: document chunking, embedding, vector search and context retrieval
- Analyze: BI and reporting, streaming analytics, data sharing and collaboration, and an agentic AI framework with multi-agent orchestration, planning, memory, tools and guardrails on LLMs and foundation models
- Integrate: Chatbots, mobile apps, web portals and APIs
- Platform foundation: Delta Lake for universal open storage; Unity Catalog for governance, security, observability, interoperability and sharing; MLflow for tracking, evaluation and guardrails; Genie spaces as an AI partner for data work
Engaging with LTTS on Databricks
Start small, prove the value, then scale on a plan you have signed off.
Discovery Sprint
4–6 weeks, fixed fee
- Assessment and TCO business case
- Target architecture and roadmap
Build Pod
Outcome-based delivery squads
- Migration, pipelines and AI use cases
- Scales up or down by wave
Managed Lakehouse
24x5 run with defined SLAs
- Cost control and FinOps reporting
- Continuous performance tuning
Your First 90 Days
Days 0–30
Assess and align
- Assess the estate and data landscape
- Agree the first two use cases
- Build the TCO and value case
Days 31–60
Build the foundation
- Stand up the Lakehouse foundation
- Deploy the Unity Catalog model
- Land the first production pipelines
Days 61–90
Go live and plan to scale
- Take the first two use cases live
- Measure business and cost impact
- Agree the scale-up wave plan
Start With a No-Cost Databricks Readiness Review
In two weeks, our Databricks architects review your current data estate, identify the highest-value first use cases and outline a TCO-backed path to the Lakehouse.
Resources
From AI Pilots to Production: The Data Mandate
Enterprise leaders are under growing pressure to turn AI experimentation into measurable business outcomes. With the focus on how the technology can improve productivity, automate processes, or unlo...
The LTTS–Databricks alliance is a joint go-to-market partnership between L&T Technology Services and Databricks. It combines the Databricks Data Intelligence Platform with LTTS’ engineering domain expertise to help enterprises migrate legacy data estates, unify IT and OT data on the Lakehouse, govern it with Unity Catalog, and take analytics and AI into production.
LTTS is an engineering services company first. Its teams understand products, plants and industrial processes, not just the platform. As a result, data models, pipelines and AI use cases reflect how equipment, production lines and supply chains actually behave, which matters most when working with OT, sensor and PLM data.
A discovery sprint takes four to six weeks and produces a TCO business case, target architecture and roadmap. Landing zone, workspace and CI/CD setup takes six to eight weeks. In a typical first 90 days, LTTS stands up the Lakehouse foundation, deploys Unity Catalog and takes the first two use cases live.
Yes. LTTS builds medallion pipelines on Databricks that ingest data from SAP, MES, historians, PLM systems, IoT gateways and edge brokers, alongside CDC, Kafka and API feeds. Production workloads include sensor and telemetry data at multiple terabytes per day, near real-time OEE and yield views, and streaming anomaly alerts routed into maintenance workflows.
LTTS uses Mosaic AI to build retrieval-augmented generation (RAG) over manuals, SOPs and service tickets, and AI agents connected to ERP, PLM and field-service systems. Every release passes an evaluation suite before launch, with guardrails, PII filtering and human review in place, and cost, latency and quality are tracked in production.
The LTTS FinOps Cockpit provides a live view of cluster, SKU and workload spend, with budgets, alerts and chargeback by business unit. Combined with Photon, cluster and query tuning and right-sizing against SLAs, it delivers 15–25% savings on run costs.
Start with a two-week, no-cost readiness review of your current data estate. LTTS architects assess your landscape, identify the highest-value first use cases and outline a TCO-backed roadmap. From there, you can move into a fixed-fee discovery sprint, an outcome-based build pod or a managed Lakehouse engagement.
LTTS provides six Databricks services: Lakehouse strategy and migration; data engineering and streaming on Delta Lake; data governance with Unity Catalog; AI, machine learning and generative AI with Mosaic AI; analytics, BI and data products; and managed Lakehouse operations with FinOps. Each service has defined deliverables, accelerators and a definition of done.
LTTS uses a four-phase, wave-based approach: assess, design, migrate and optimize. Automated conversion of code, ETL and SQL with built-in test packs, parallel runs and row-level reconciliation remove big-bang risk, and legacy systems are switched off only after each wave is validated. Automation reduces migration effort by 30–40%.
LTTS delivers Databricks programs for industrial products, medtech and healthcare, mobility and automotive, aerospace and defense, semiconductors, energy and plant engineering, and hi-tech and software companies. Typical use cases include predictive maintenance, OEE analytics, vehicle telematics, fab yield analytics, GxP-ready clinical data platforms and emissions reporting.
LTTS designs Unity Catalog governance to produce audit evidence from day one. This includes control mappings for GxP, IEC 62304 and ISO 27001, GDPR and data-residency patterns, row filters and column masking for personal data, and end-to-end lineage and access history. The LTTS Governance Blueprint helps clients become audit-ready in about six weeks.
LTTS deploys Databricks on AWS, Microsoft Azure and Google Cloud, using security-hardened landing-zone templates for each. Unity Catalog provides a single governance layer across clouds and workspaces, and Delta Sharing lets organizations share data with partners without copying it.
The LTTS Databricks practice has 159 dedicated practitioners, including 43 professionals certified as Databricks Data Engineer (Associate or Professional) and 12 solution architects who lead client engagements. The team also holds 25 cloud and data platform certifications, with 25 more in progress.