Why Enterprises Need a Data Platform Like Databricks, and What They Actually Get From It

1. Why you need a data platform

We see the same story again and again. Leadership asks for “something with AI.” IT builds a pilot, the demo looks great, and it never reaches production. The root cause is almost always the same: AI is only as good as the data it can reach.

Signs you need a data platform:

• Every department has its own numbers. Sales runs on CRM, finance on ERP, operations on spreadsheets. Meetings are spent arguing about whose figures are right.

• Reports take days. A simple question from leadership goes through a BI team that stitches exports together by hand.

• Every AI pilot starts from scratch. Each one builds its own data connections and copies. Three pilots in, you have three new silos and nothing reusable.

• Nobody knows exactly who accesses what. Under GDPR, NIS2, DORA and the EU AI Act, that’s a growing liability. With AI, you also need to prove which data an answer came from.

• The warehouse can’t handle AI workloads. It does tables and reports, but not the contracts, emails, documents and logs where AI adds the most value.

What a data platform fixes:

1. One source of truth. Data from every system in one model, with clear definitions: what exactly counts as an “active customer”?

2. One governance layer. Who sees what, where data came from and who changed it, for tables, documents and AI models alike.

3. Analytics and AI on the same data. No copying data into yet another AI tool.

4. Reuse. Your second, fifth and tenth AI use case build on the same foundation, and each ships faster than the last.

This architecture is called the lakehouse. It combines the strengths of a data warehouse (structure, performance, reporting) with those of a data lake (any data type, scale, AI). Databricks created the concept.

2. Why Databricks

In Central Europe, Databricks is most often compared with Microsoft Fabric and Snowflake. Here’s why we recommend Databricks to organizations that are serious about AI.

Model choice: no lock-in to a single AI vendor

To us, this is the strongest argument. Databricks gives you models from every major provider in one place:

• Anthropic Claude, OpenAI GPT, Google Gemini, Meta Llama, Qwen and others, hosted directly on the platform,

• external models via Azure OpenAI, Amazon Bedrock or Google Vertex AI,

• your own custom and fine-tuned models.

They all go through one AI Gateway, with shared rate limits, monitoring, guardrails and audit logs.

Why it matters: AI models change faster than IT strategies. Today’s best model may be outclassed in six months. If an application is hard-wired to one provider, every switch means a rewrite. On Databricks, swapping models is a configuration change. You can benchmark the same task, such as complaint triage or contract summarization, across three models and keep the one that performs best.

EU data residency: some of the newest models run on global endpoints and require cross-geography processing to be enabled. If regulated data must stay in the EU, check this per model up front.

Open formats: your data stays yours

Data sits in your own cloud storage in open formats (Delta Lake and Apache Iceberg) that other engines can read directly. If you ever leave, you don’t have to extract your data from a proprietary system first.

One governance layer for data and AI

Unity Catalog manages permissions, lineage (where data came from and where it flows), auditing and sensitive-data tagging, for tables, files, AI models and agents. When auditors ask who accessed what and which data an AI answer was grounded in, you can show them.

One platform instead of ten tools

Without a platform, enterprises assemble storage, orchestration, data quality, ML model management, lineage, AI serving and BI from ten separate products. Each has its own access model, contract and specialist. Databricks is one environment, which means fewer brittle integrations and faster delivery of new use cases.

Works with what you already have

Databricks runs on Azure, AWS and Google Cloud, and it is a first-party service on Azure, where most CEE enterprises sit. You don’t have to abandon Microsoft either: results show up in Power BI, Databricks reads Microsoft Fabric (OneLake) data without copying it, and Genie works inside Microsoft Teams.

3. Built-in tools: Genie and more

Much of what enterprises used to commission as custom software now ships as ready-made products on Databricks. The implementation work isn’t building them from scratch. It’s preparing the data, connecting the tools to your systems, encoding your business definitions and getting people to use them, and that is what decides whether a tool delivers value or sits unused.

Genie: ask your data

Databricks Genie is an AI analyst you can ask about company data in plain language. Genie translates the question into SQL, runs it against your data, and returns text, a table or a chart. It always shows how it got there, so the answer can be checked. For example:

• “How did revenue in our Polish subsidiary develop this quarter versus last year?”

• “Which customers cut their orders by more than 20%?”

• “Why did the B2B margin drop in Q3?”

Genie is now a family of products rather than a single tool:

Why Genie is enterprise-safe: each user only sees data they’re permitted to see in Unity Catalog. A regional manager sees only their region, even when asking the same Genie Agent as the CEO. Generated queries are always read-only, and each one is logged.

Genie in non-English languages

Genie works in languages other than English, so your teams can ask questions in Polish, Czech, Slovak, Hungarian or German. Databricks is open about the limitation, though: the underlying system prompts are in English, so responses may sometimes come back in English. Its recommendation is to add as much metadata as possible in your users’ language.

For multilingual CEE organizations, that’s where most of the Genie implementation work goes:

1. Local-language table and column descriptions, so Genie knows that rev_net means “net revenue” in the language your users speak.

2. Synonyms. Revenue, turnover and sales all mean the same thing, and so do branch, outlet and store.

3. Format instructions for local date formats, currencies (EUR, CZK, PLN, HUF), decimal commas, fiscal years and regional names.

4. Trusted assets. Verified query logic for the most common questions, so the answer is consistent and correct every time.

5. A benchmark before go-live. We collect real questions from your people in their own language and measure how many Genie answers correctly. It goes live only once the results hold up.

The catch: Genie is only as good as your data and definitions. If a company calculates revenue three ways, it will get three answers. Every Genie rollout therefore starts with the data model and a business glossary. Unity Catalog Metrics help here by defining KPIs once, centrally, for Genie, dashboards and AI agents alike.

Other built-in tools we deploy

The big advantage of built-in tools: the first visible result arrives within weeks, not after months of development.

4. Custom AI development on Databricks

Built-in tools cover common needs. Your competitive edge is in processes only you run: how you plan production, price risk, serve customers or run your branches. For those we build custom AI solutions on the same data, security and governance, so a custom solution never becomes another silo.

Examples from our work

• Sports betting group (CEE): AI assistant for analysts. Analysts ask questions in natural language instead of queuing for reports from the data team.

• Sports betting group: customer-support AI agent. The agent answers customers from the company knowledge base. We measured and tested its answer quality before it ever met a real customer.

• Retail (UK): AI decision engine for stores. We unified POS, workforce, finance, CRM and external signals (weather, holidays, local events) into one governed model on Databricks. On top of it runs a 28-day staffing forecast by store, hour and role. Store managers accept or dismiss each recommendation, and every response trains the system further.

• Telecommunications (Austria): AI strategy. We mapped and prioritized AI initiatives by value and recommended Databricks as the target platform on top of the existing warehouse.

How we build AI that reaches production

• Human in the loop. AI recommends and people decide, at least until the system has earned trust.

• Shadow mode first. A new model runs silently alongside the current process and is promoted only once it measurably beats it.

• Measurable quality. We evaluate AI agent responses continuously with MLflow instead of eyeballing them.

• Model independence. AI Gateway lets us swap in a better model as soon as one appears.

• Everything as code. Pipelines, models and deployments are versioned and reviewed (Databricks Asset Bundles, CI/CD).

Our rule: use the built-in tool first, and build custom only where it sets you apart.

How to get started

1. Discovery workshop. Together we map your data, systems, regulatory requirements and the places AI would add the most value.

2. Target architecture and roadmap. What to deploy right away (e.g. Genie over sales data), what to build custom, and in what order.

3. First use case in production. One real problem solved for real, not a slide-deck demo.

4. Scale and knowledge transfer. More domains, more tools and a steadily stronger internal team.

Looking for a Databricks implementation partner in Central & Eastern Europe? Sudolabs combines certified Databricks expertise with AI product engineering. We work across the wider CEE region, US and the UK. Book an intro call →

FAQ

What is Databricks?

Databricks is a data and AI platform that combines data storage and processing, analytics, machine learning and generative AI in one place. It runs on Azure, AWS and Google Cloud and stores data in open formats in your own cloud account.

Is Databricks only for large enterprises?

No. You can start with one concrete use case and expand from there. Organizations with data spread across many systems that want to build AI on top of it benefit the most.

What’s the difference between Databricks and Microsoft Fabric?

Fabric excels at integration with Power BI and Microsoft 365. Databricks is stronger in data engineering, machine learning and AI, and offers broader AI model choice. The two can be combined: Databricks reads OneLake data without copying it, and results can be shown in Power BI.

Can I use ChatGPT, Claude and Gemini on Databricks?

Yes. Databricks serves models from OpenAI, Anthropic, Google, Meta and others through one interface, so you can switch models without rewriting applications.

What is Databricks Genie?

Genie is an AI analyst in Databricks. You ask a question about company data in plain language and get an answer as a table or chart, with an explanation of how it was produced. Each user only sees data they’re permitted to access. Genie is also available in Microsoft Teams.

Does Databricks Genie work in languages other than English?

Yes, users can ask questions in other languages. Genie’s underlying system prompts are in English, so responses may sometimes come back in English. Local-language table and column descriptions, synonyms, instructions and trusted assets make it much more reliable.

Does my data stay in the EU?

Databricks runs in EU regions on all three major clouds. For some of the newest AI models, check whether cross-geography processing is required.

Do I need my own data team?

Not at the start. A partner can build and run the platform. We recommend gradually building at least a small internal team, and knowledge transfer is part of our engagements.