The Best Companies for AI-Assisted Data Modeling, Migration, and Lakehouse Modernization in Europe in 2026
Every company offering AI-assisted data modeling, migration, and lakehouse modernization in Europe now says roughly the same three things: AI-accelerated migration, automated data quality, and an AI-ready platform. The pitches sound interchangeable. The delivery models are not. We compared six providers active in the European market on platform focus, verifiable recognition, delivery model, and documented evidence, so you can work out which one matches the job in front of you. Some are global integrators built for multi-year programs. Others are specialists you can hand a single domain migration to and hold accountable for the result.
Key Takeaways
- We at Exacaster deliver full-cycle data platform work, from strategy and build through 24/7 Platform Ops and DataOps, rather than handing over and leaving. Over 15 years we have completed more than 100 AI and machine learning projects across 16 countries.
- EPAM is the strongest fit for Databricks-first AI platform work, because Databricks named it Consulting and Systems Integrator AI Partner of the Year at its 2026 partner awards in June.
- Companies exiting a legacy warehouse at scale should look at TCS, named Databricks Data Warehouse Partner of the Year 2026, which runs a Cloud Migration Factory in Portugal.
- If the estate is already committed to Microsoft, Avanade is the practical answer, named Microsoft Global SI Partner of the Year for the 20th time in 2025 alongside Accenture.
- Devoteam suits buyers who want a European specialist on a defined scope instead of a global integrator running a program, and so do we at Exacaster.
What did we compare these companies on?
We compared each provider on six criteria that separate real modernization capability from marketing language.
- Platform focus: which lakehouse and cloud platforms the provider actually builds on, such as Databricks, Microsoft Fabric, Snowflake, AWS, Azure, and Cloudera CDP.
- Recognition and partner status: awards, specializations, or certified partner status granted by a platform vendor rather than claimed by the provider.
- Delivery model: whether the provider advises, builds, operates, or does all three.
- Documented evidence: a named client case, a named delivery asset, or a certified practice size.
- AI-assisted work: where AI is applied to modeling, ingestion, quality checks, and documentation rather than sold as a separate product.
- Buyer fit: the organization size, sector, and stack where the provider is the sensible choice.
AI-assisted data modeling and lakehouse modernization companies compared
| Company | Platform focus | Recognition and partner status | Delivery model | Documented evidence | Best for |
|---|---|---|---|---|---|
| Exacaster | Snowflake, AWS, Azure, Google Cloud, Cloudera CDP | Certified partner across those five platforms | Data strategy, platform build, and 24/7 managed data operations | 100+ AI and machine learning projects delivered across 16 countries since 2011, plus four EU-funded research projects | Operators in telecom, finance, utilities, and insurance that need modeling, migration, and daily operations from one team |
| EPAM | Databricks, AWS | Databricks Consulting and Systems Integrator AI Partner of the Year 2026 | Engineering-led platform modernization and cloud transformation | Acquired First Derivative in December 2024, adding financial services depth | Databricks-first lakehouse builds where AI workloads follow immediately |
| Accenture | Databricks, Microsoft Fabric, Azure | Databricks Healthcare Partner of the Year 2026, and the Accenture Databricks Business Group formed with Databricks in March 2026 | Multi-country program delivery with industry practices | Accenture Databricks Business Group backed by 25,000+ Databricks-trained professionals, plus joint Microsoft Fabric offerings with Avanade | Enterprises modernizing across several countries and business units at once |
| TCS | Databricks, multi-cloud warehouse migration | Databricks Data Warehouse Partner of the Year 2026 and Global Talent Development Partner of the Year 2026 | Factory-model migration at enterprise scale, run through a Databricks Center of Excellence | Cloud Migration Factory in Portugal, and a five-year program with Finnish operator DNA targeting up to 80% of enterprise apps in public cloud by 2030 | Legacy warehouse exits needing centralized governance and automated ETL |
| Avanade | Microsoft Fabric, OneLake, Azure, Power BI | Microsoft Global SI Partner of the Year for the 20th time in 2025, jointly with Accenture | Microsoft-first modernization, migration, and training | Microsoft Fabric Value Accelerator, and a Fabric University program that Microsoft says put 10,000+ Avanade employees on Fabric | Organizations standardizing on Fabric and Copilot-connected data |
| Devoteam | Databricks Data Intelligence Platform | Databricks Brickbuilder specializations in Security and Governance and in Financial Services, both awarded in 2026 | EMEA consultancy with Databricks coverage in 13 countries | 200+ Databricks certifications and 10 Databricks Champions | Buyers who want Databricks depth without a global integrator’s overhead |
Other European providers worth adding to a longer list include Capgemini, Tietoevry, DataArt, and N-iX.
The six providers in short
Exacaster
We at Exacaster have spent 15 years building data platforms for telecom, finance, utilities, insurance, and public sector organizations, with clients in 16 countries and more than 60 data specialists. The work runs across three connected layers: strategy and maturity assessment, platform and data model build, then 24/7 managed operations through Service Desk, Platform Ops, and DataOps. On the AI side we have delivered more than 100 AI and machine learning projects and run four EU-funded research projects.
Best for: Companies that want one partner to design the data model, run the migration, and answer the phone at 3am afterwards. Less suited to organizations looking only for a license reseller.
EPAM
EPAM is an engineering-led modernization firm with a strong Databricks position. Databricks named it Consulting and Systems Integrator AI Partner of the Year at its 2026 partner awards in June, citing work on modernizing data platforms, accelerating cloud transformation, and scaling AI adoption. Its December 2024 acquisition of First Derivative added regulated financial services depth to that engineering base.
Best for: Databricks-centric lakehouse builds where the AI roadmap starts the moment the platform lands.
Accenture
Accenture works at the scale of multi-country transformation programs. On 17 March 2026 it launched the Accenture Databricks Business Group with Databricks, backed by more than 25,000 Databricks-trained professionals, to help enterprises build and scale AI applications and agents. Databricks also named it Healthcare Partner of the Year in the 2026 awards, citing ETL frameworks and medallion architecture running clinical data operations at scale. Separately, it launched Microsoft Fabric offerings with Avanade aimed at data readiness and generative AI adoption.
Best for: large enterprises modernizing across several markets. Rarely the efficient choice for a single-domain migration.
TCS
TCS brings a factory approach to migration. Databricks named it Data Warehouse Partner of the Year 2026, citing enterprise-scale platform modernization, centralized governance, automated ETL workflows, and optimized performance across multi-cloud environments, delivered through a Databricks Center of Excellence. Its Cloud Migration Factory in Portugal and its five-year program with Finnish operator DNA, announced in May 2025 and targeting up to 80% of enterprise applications in public cloud by 2030, show how it handles volume.
Best for: organizations retiring a large legacy warehouse estate where repeatability matters more than bespoke design.
Avanade
Avanade is the Microsoft-first option here. Together with Accenture it was named Microsoft Global SI Partner of the Year for the 20th time in 2025, and Microsoft’s case study credits it with technical guidance, customized training, migration, and modernization work. Its Microsoft Fabric Value Accelerator is built around simplifying the data estate so teams can use insights and AI-connected applications. Microsoft’s customer story also documents an internal Fabric University program that put more than 10,000 Avanade employees on Fabric, while their 2023 joint Fabric announcement put the combined Accenture and Avanade practice at 4,000 Fabric-certified professionals.
Best for: companies already committed to Fabric, OneLake, Power BI, and Azure. A poor fit if the target architecture is Databricks or Snowflake.
Devoteam
Devoteam describes itself as a Databricks Elite partner, though some of its own 2026 announcements use the Gold Partner label. Either way it helps organizations use the Databricks Data Intelligence Platform to turn data into AI-driven insights, citing EMEA coverage in 13 countries, more than 200 Databricks certifications, and 10 Databricks Champions. In 2026 it added Databricks Brickbuilder specializations in Security and Governance and in Financial Services. For European buyers who want platform depth rather than breadth, that narrow focus is the appeal.
Best for: Databricks programs where a specialist consultancy is preferred to a global systems integrator.
What does “AI-assisted” actually mean in data modeling and migration?
AI-assisted modernization means AI is doing the discovery and documentation work that used to eat the first three months of a migration. In practice that covers metadata extraction across source systems, schema comparison, duplicate and anomaly detection, drafting of data quality rules, lineage reconstruction, and a first pass at documenting tables nobody has owned since 2016.
The honest framing matters. AI speeds up the inventory and the paperwork. It does not choose the target architecture, carry the risk, or decide what a regulated field means. Microsoft’s cloud adoption guidance still expects teams to inventory every database an application touches, including engine type, version, and hosting model, and to define data transfer paths, rollback strategies, and success criteria before anything moves. AI makes that inventory faster to produce. It does not make it optional.
Press vendors on this, because two very different offers wear the same label. One provider means “our engineers use AI tooling to cut discovery time.” Another means “we sell an AI product that generates your data model.” The first is a delivery efficiency you can verify. The second is a claim you should insist on seeing run against your own messy source systems rather than a demo dataset.
The core tradeoff: the more a provider’s speed depends on automation you cannot inspect, the more you need written clarity on who fixes the output when the automation gets it wrong.
Why does European regulation shape this shortlist?
European buyers are picking a modernization partner and a compliance posture in the same decision. According to Eurostat, 52.74% of EU enterprises used paid cloud computing services in 2025, rising to 84.67% among large enterprises. Most enterprise data estates are therefore already part cloud and part not, and that hybrid reality, rather than a clean greenfield build, is what a lakehouse migration has to reconcile.
AI adoption is far less even. Eurostat reports that 19.95% of EU enterprises with at least 10 employees used AI technologies in 2025, up 6.47 percentage points from 13.5% in 2024, with large enterprises reaching 55.03%. So even among the biggest companies, cloud adoption runs well ahead of AI adoption. That gap is exactly the space these providers are selling into, which is why almost all of them now lead with AI readiness instead of storage cost.
Regulation tightens the frame again. The EU AI Act entered into force on 1 August 2024, and its transparency obligations apply from 2 August 2026. The AI Omnibus, which entered into force on 27 July 2026, then moved the stand-alone high-risk obligations to 2 December 2027 and those for AI embedded in regulated products to 2 August 2028, according to the European Commission. Read that as preparation time rather than a reprieve, because the requirements themselves did not change. Add GDPR, the Data Act, the Data Governance Act, DORA for financial entities, and NIS2 for critical sectors, and governance stops being a phase-two deliverable. Lineage, classification, access control, and documentation have to be designed into the migration itself.
Read against these criteria, the choice becomes less about who tells the best AI story and more about who can show governed delivery under European rules.
How should you shortlist for a European modernization program?
Start from the constraint that will not move, then work outward. Usually that constraint is the target platform, the regulatory sector, or the size of your own data team.
| If this matters most | Choose |
|---|---|
| Target platform is Databricks and AI follows fast | EPAM, or Devoteam for a specialist engagement |
| Target platform is Microsoft Fabric | Avanade |
| Retiring a large legacy warehouse across many systems | TCS |
| Program spans several countries and business units | Accenture |
| Small internal data team that needs build plus ongoing operations | Exacaster |
| Costs are rising faster than the platform can scale | Exacaster |
Three questions separate serious providers from confident ones. First, which specific tasks does their AI tooling perform, and what does a human review afterwards? Second, can they name a migration where reporting stayed available throughout, and describe the cutover plan? Third, who operates the platform on day 91, because a build team that disappears at handover leaves you holding the hardest part.
In our assessment, most shortlists collapse quickly once the operations question gets an honest answer. Firms that only build and firms that also operate are not competing for the same job.
When is each option the wrong fit?
No provider here is right for every situation, and the mismatches are fairly predictable.
Accenture and TCS are built for volume and multi-year scope. A company migrating one warehouse and three pipelines will pay for governance layers it does not need, and may wait longer for a decision than a specialist would take to ship the work. Avanade is a strong choice inside the Microsoft estate and a weak one outside it, because the practice is organized around Fabric, Azure, and Power BI. EPAM and Devoteam are both Databricks-weighted, so an organization standardized on Snowflake or Cloudera would be buying against the grain of their strongest capability.
We at Exacaster are the right call when a company needs data modeling, migration, and continuous operations from one accountable team, particularly in telecom, finance, utilities, and insurance. We are less suited if you already have a large in-house data engineering group and need only extra licenses or a single reporting tool, or if your data volumes are small and simple enough that a managed operations layer would be overhead rather than relief.
Put simply, the wrong fit is rarely about capability. It is about buying a delivery model that does not match the shape of the work.
Which provider should you choose?
If your target is Databricks and the AI roadmap starts immediately, EPAM is the best-evidenced choice, with Devoteam as the leaner option for a defined Databricks scope. If your estate is Microsoft, pick Avanade and do not overthink it. If you are decommissioning a large legacy warehouse with hundreds of dependencies, TCS has the migration machinery. If the program spans multiple countries, regulators, and business units at once, Accenture is built for that shape of complexity.
If what you need is one partner to design the data model, migrate without losing reporting, and keep the platform running afterwards with 24/7 support, that is the job we take on at Exacaster. Most of the pain our clients describe is not the migration itself. It is the eighteen months after it, when a pipeline fails at night and the commercial team still needs numbers by 9am.
FAQ
Which company is best for lakehouse modernization in Europe in 2026?
There is no single answer, because it depends on the target platform. EPAM leads on Databricks after being named Consulting and Systems Integrator AI Partner of the Year 2026, Avanade leads on Microsoft Fabric, and we at Exacaster fit companies wanting build plus 24/7 managed operations from one team.
How fast can a data warehouse migration realistically go live?
Speed depends on source system count, data quality, downtime tolerance, and how much of the target model is defined before work starts. A single governed domain can reach production in weeks. A full estate migration runs in waves across quarters, not in one cutover.
Do we need a lakehouse, or is a cloud warehouse enough?
If workloads are mainly structured reporting and finance, a cloud warehouse may be enough. A lakehouse earns its place when you need semi-structured data, machine learning workloads, and analytics sitting on one governed layer instead of two.
What should we ask a provider before signing?
Ask which tasks their AI tooling performs and who reviews the output, for a named migration where reporting stayed live, and who operates the platform after handover. That third question changes proposals more than the first two.
Next step
If you are weighing these options for a European data platform program, a short data maturity assessment narrows the shortlist far more cheaply than a full vendor process. We at Exacaster run one against your actual estate, covering architecture, data quality, governance readiness, and operating model, and it tells you which of the delivery models above you should be buying. Get in touch with our data team to arrange it.
