One accountable Databricks professional systems integrator, from migration to governed production AI
We help enterprises put Databricks to work as more than a data platform. Instead of stopping at migration, we take on the full Databricks services lifecycle: Assessing your data estate, modernizing it, embedding governance from day one, putting enterprise AI and agents into production, and then running the platform so it keeps delivering.
Most enterprises come to us running fragmented systems for extract, transform, load (ETL), warehousing, machine learning and reporting, with AI stuck in pilot and costs that are hard to predict. Our Databricks migration services consolidate that estate onto one governed lakehouse, using our own AI-accelerated migration and ingestion frameworks to get you there faster.
We're a Gold Databricks Partner with all four 2026 Databricks Brickbuilder Specializations.
The Department of Mobility and Public Works modernized its data analytics platform on Azure and Databricks to spot road safety risks faster and deliver better public services.
One of Italy’s leading telecommunications providers, the company serves millions of residents, businesses and public institutions. To deliver broadband services, cloud solutions, security offerings and even develop innovative initiatives — including Italy’s first large language AI model — the company relies on vast amounts of data.
Databricks provides the toolkit, and DXC engineers use it to deliver the outcome you need. With our enterprise Databricks consulting services, you’ll work with one accountable team across the whole lifecycle.
We assess, architect, build, migrate, govern and run your Databricks platform, then put models and agents into production. That's different from working with a specialist who can pilot but can't operate, or a migration partner who moves your technical debt along with your data.
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We hold Gold Databricks Partner status and all four 2026 Databricks Brickbuilder Product Specializations. We're also a confirmed Databricks Lakebase launch partner.
Our AI Data Ingestion Framework, migration frameworks and MLOps accelerator are built to speed up delivery.
Consolidating data and AI on one platform sounds simple. In practice, fragmented estates fight the process at every step. Here's how we help you get past these obstacles:
Fragmented, costly legacy estates
Separate systems for ETL, warehousing, analytics and AI carry separate definitions, drive up cost and slow everything down. Engineers spend their weeks nudging pipelines, and every new source is rebuilt from scratch. We architect one governed lakehouse under Unity Catalog and deliver a reusable conversion factory — definitions live with the data, every consumer inherits one set of rules, and the second source costs materially less than the first.
More systems in, nothing switched off
Every program adds a platform without retiring one. The migration that was supposed to simplify the estate never quite finished, and the team learns about failures from the users it's failing. Our Databricks AI services sequence conversion waves so legacy can actually be decommissioned, with evidenced equivalence, a rehearsed rollback, and observability built in from day one.
Unpredictable platform costs
Databricks on one invoice, cloud on another, professional services on a third — and none of them is a project bill. Dual-run keeps two platforms and two teams alive with no end date. We profile the estate before we price the move, tag compute, storage and AI tokens to the workload that triggered them, and join telemetry to the cloud bill so cost attribution becomes actionable. The conversion runs as a factory program with a finish line, giving dual-run a stated end date and a people cheque that shrinks.
We assess your estate, architect the governed lakehouse, convert workloads in controlled waves and hand off a bounded service, whether you're migrating legacy or building from scratch.
Agentic, standards-aligned ETL pipeline, data model and KPI creation that reduces time to market while ensuring governance adherence. Reusable patterns mean the second and third data product cost materially less than the first.
Decision-support apps, what-if scenario modelling and simulation built on governed, reusable data products, that turns the Databricks Lakehouse into answers the business can act on, not just tables it can query.
Faster AI use-case delivery with model, agent and prompt governance, evaluation and guardrails built in. Models and agents move from pilot to governed production, not from pilot to pilot.
Semantic models, ontologies and AI context assets that make governed data understandable to people and agents, so the intelligence layer inherits your definitions rather than inventing its own.
Data product catalog, publishing and the ability to maintain contracts, plus the operating model and enablement content to adopt and sustain it. Products stay governed after the project team leaves.
We take production ownership of the Databricks Lakehouse, with accountability, named service owners, observability that catches issues before users do, two-bill FinOps with challengeable unit economics, and human-in-the-loop approval on every consequential change.
SAP data landed in Unity Catalog, rebuilt as bronze to silver to gold data products, reconciled against source. Zero-copy share is the landing, not the finish line.
Object-by-object conversion: keep & migrate, simplify, rebuild or retire. Assessment is station one, dual-run gets an end date.
Delta tables plus Databricks SQL, procedures re-engineered rather than cloned. Wave packs from inventory through equivalence to acceptance.
DXC is a fast‑ramping Databricks Gold Partner, demonstrating strong momentum across skills, solutions, joint customers and go‑to‑market execution. Through sustained investment in certifications, champions and Brickbuilder specializations, DXC helps clients scale Databricks faster and deliver enterprise‑grade outcomes across industries.
DXC’s Databricks services are designed for organizations that are modernizing legacy or fragmented data platforms while preparing their data foundations for advanced analytics and AI. We partner with global clients across diverse industries to support their transition from legacy or on‑premises environments to Databricks, enabling more scalable and flexible data architectures.
In doing so, DXC helps organizations establish trusted and well-governed data foundations that can reliably support both analytics and AI initiatives. Building on this foundation, we work with clients to develop and scale AI and generative AI applications, including copilots and retrieval-augmented generation (RAG) solutions, tailored to business needs.
Beyond development, DXC focuses on operationalizing AI by implementing automated, agentic workflows that embed intelligence into day-to-day processes. We also enable seamless integration of SAP and non-SAP data using Databricks and SAP Business Data Cloud, ensuring a unified and accessible data environment. Finally, DXC accelerates delivery of industry-specific use cases through proven Databricks accelerators, helping clients achieve faster time to value and measurable outcomes.
By combining SAP BDC with Databricks, organizations will realize the full value of SAP data at scale. The SAP–Databricks partnership unifies SAP and external data without costly workarounds (Unified Data), ensures compliant governance and discoverability (High Trust), and operationalizes analytics, ML and agentic AI across the enterprise (Scalable AI).
DXC believes that the best outcomes come from applying the right platform for the right use case — using SAP Databricks to preserve the SAP‑native business context, and extending to Enterprise Databricks to blend the SAP and non‑SAP value‑stream data for better decisions across end‑to‑end processes. We have put this to the test with our manufacturing clients, using Databricks to ingest millions of shop floor and IoT signals and enrich them with SAP data — such as maintenance plans, production schedules and bills of materials (BOMs) — to power predictive insights and closed‑loop actions.
DXC can help transform your business by:
In this paper, Enterprise Data Solution Architect Arun Khandelwal addresses the challenges of traditional models and explores how to ensure data quality, security and accessibility with Databricks’ data intelligence platform.
GenAI projects fail when data is scattered, governance is unstable and teams juggle too many tools. With a single, unified AI platform, DXC and Databricks help guide users to project success.
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DXC's Databricks services cover the full data and AI lifecycle on the Databricks Data Intelligence Platform, from Databricks consulting, including assessing a legacy data estate, to migrating it to building governed AI and agents in production.
The work spans three areas: Data engineering services and platform modernization, AI and machine learning engineering and agentic AI automation through DXC's AI Workbench. Instead of treating Databricks as a lift-and-shift target, DXC modernizes the underlying data engineering, applies Unity Catalog governance from the start, and takes AI models and AI agents into production.
DXC backs this with its own AI-accelerated migration and ingestion frameworks, built specifically to speed up delivery on Databricks.
Most enterprise AI initiatives on Databricks stall after the pilot stage because there is no governed feature layer, no reliable MLOps process and no clear owner for running the model once it is built.
DXC's AI and machine learning engineering team takes models from pilot through training, serving and MLOps at scale, using its own MLOps accelerator to standardize deployment. DXC's AI Workbench then builds and governs the natural-language agents that put those models in front of business users, with Unity Catalog controlling who can access what.
This full-lifecycle approach is why DXC positions itself as an operator of production AI on Databricks, not just an implementer of pilots.
DXC delivers the entire Databricks lifecycle as one accountable team: Assessment, migration, governance, AI and agent deployment, and ongoing operations. Many partners specialize in a single phase, most often migration, and hand off production support and AI enablement to someone else.
DXC is a Gold Databricks Partner with all four 2026 Databricks Brickbuilder Specializations, and it applies its own AI-accelerated migration frameworks, including Rapid Migrator and the Analytics Modernization and Migration Factory, to cut migration effort.
DXC also runs its own Databricks migration internally, using that experience to refine the frameworks and accelerators it applies to customer engagements.
DXC migrates legacy platforms, including mainframe, Teradata, Oracle, SAP BW, Hadoop and Informatica environments, onto Databricks using its own migration frameworks alongside Databricks Lakebridge.
The frameworks, including Rapid Migrator and the Analytics Modernization and Migration Factory, automate much of the schema conversion, pipeline rebuild and validation work that normally consumes the bulk of a migration project.
DXC pairs the migration with governed ingestion and pipeline engineering, so the resulting Databricks environment is production-ready and cost-managed from day one.
Enterprises evaluating a Databricks services partner should look beyond day rates and licensing costs to total cost of ownership, migration experience, governance expertise and the ability to run the platform after go-live — not just build it.
A partner that only handles migration or implementation will often hand off governance, cost optimization and AI enablement to someone else, which is where many Databricks programs stall after an initially successful launch. Long-term support, Unity Catalog governance expertise, and proven AI and machine learning delivery matter as much as migration speed.
DXC is built around the full lifecycle, not a single phase: Assessment, migration, governance, AI and agent deployment, and ongoing managed services. We are also a confirmed Databricks Lakebase launch partner, reflecting how closely we work with Databricks on new capabilities as they reach market.
DXC implements Databricks Unity Catalog as a governed context layer, embedding lineage, access control and compliance from the start of an engagement instead of retrofitting them once systems are already in production.
That context layer turns raw tables into business-ready, reusable data and AI assets, with business definitions mapped to physical data so agents, analysts and applications all work from the same trusted definitions. On the cost side, DXC's FinOps practice monitors usage, performance and spend once the platform is live, targeting run-cost savings of 40% or more compared with traditional delivery, with one benchmark engagement reporting run costs more than 10 times lower.
This combination of governance and cost management is part of DXC's managed services offering, so customers get ongoing oversight, not a one-time setup.
