Databricks
Organizations often work with separate, patched-together tools for data, which means overview is lacking and AI projects run aground on a weak data foundation. Databricks brings all layers, from raw data to self-built AI Agents, together in one platform.
What is Databricks
Databricks is an integrated data platform that brings together data storage, processing, analytics, machine learning, and Gen AI in one environment. No separate tools stitched together with custom work, but one platform with shared overview, version control, and security.
Important: Databricks does not replace your existing Power BI or Qlik environment, but complements it. You build your data model once in Databricks and then use it directly in Power BI or Qlik, with the same access rules via Unity Catalog. This way you keep your existing dashboards and team knowledge, and add a governed data foundation layer underneath.
The benefits of Databricks
One platform, full overview
One powerful platform instead of multiple separate tools, with full overview of your data, models, and AI agents thanks to strong governance via Unity Catalog.
Roll back errors without data loss
Errors in data processing pipelines can be rolled back without data loss, so you work on your pipelines with more confidence.
Full compliance visibility
Data lineage and access control are fully visible, so you answer compliance questions in seconds.
Everything on one platform
Reports, analyses, and AI applications run directly on the platform, without separate environments causing synchronization issues.
Concrete examples of what you can do with Gen AI in Databricks
- With Genie Spaces you ask questions of your data in plain language, including the one-off questions a dashboard doesn't answer. This catches exactly the questions that would otherwise land on an analyst's desk.
- With Agent Bricks you build your own AI Agents, such as a Knowledge Assistant that gives employees direct answers based on internal documentation, or a supervisor agent that directs multiple specialized agents in one workflow.
- With AI Functions you call a language model directly from a SQL query, for example to classify thousands of customer reviews at once, without Python or separate tools.
- With AI you quickly build dashboards and apps on the data models already in your Lakehouse.
All of this runs under the governance of Unity Catalog, with which you determine exactly who may use which data, models, and agents.
Components of Databricks
Databricks consists of a number of components that together consolidate your data landscape, from storage to AI.
MLflow is Databricks' open source framework for the full lifecycle of machine learning and AI Agents. You can compare it to Git, the well-known version control system for software: MLflow keeps track of which version of a model or agent is running, how it performs, and when to roll back to an earlier version. This way you always know why an AI answer changes.
Mosaic AI is Databricks' broader suite for building and running GenAI applications, made up of four building blocks: ready-made language models (including Anthropic's Claude, also available within the EU), AI Functions to call models from SQL, Vector Search to connect models to your own data, and Agent Bricks to combine all of this into a working AI application.
Agent Bricks is the part of Mosaic AI with which you build, deploy, and manage AI Agents without programming. Think of a Knowledge Assistant that gives employees answers based on internal documents, or a supervisor agent that directs multiple specialized agents within one workflow. Everything is automatically version-controlled in MLflow.
With Genie Spaces you ask questions of your data in plain language, without having to write SQL yourself. This catches the questions a fixed dashboard doesn't cover, with the same access rules as the rest of your Lakehouse.
Unity Catalog is the governance layer that gives overview of who may use which data, models, and agents, with full data lineage and access control. This is the layer that connects all the other components of Databricks with each other.
Our approach with Databricks
A data platform only delivers value when it fits what's already in place. With more than 25 years of experience in data management, analytics, and application development, we first look at your biggest pain point: fragmented connections, lack of overview, or the wish to build your own AI Agents on your own data. Then we design an architecture that fits your environment, while keeping your existing Power BI or Qlik dashboards, and we first build a Proof of Concept around the most urgent bottleneck.
Only once that has proven its value do we scale up step by step. Data management and access control are in order from day one, so you stay in control as your platform grows. We implement Databricks GDPR-proof and hosted in Europe, and are certified to ISO 27001, ISO 9001, and NEN 4400-1.
FAQ
What is the difference between Databricks and a traditional data warehouse?
A traditional data warehouse is mainly suited for processed, structured data. Databricks works with a lakehouse architecture, in which raw and processed data, real-time streams, and batches come together in one storage layer. As a result, you no longer need a separate platform for analytics and AI.
Can we combine Databricks with our existing data platforms, such as Azure or Power BI?
Yes. Databricks runs on the major cloud platforms, including Azure, and connects to existing reporting tools. During implementation we always look at what you already have in place, so Databricks connects to it logically instead of replacing it.
Is our data safe in Databricks?
Security doesn't depend on the platform alone, but on how you set it up. Data lineage and access control are fully visible in Databricks. We implement GDPR-proof and hosted in Europe, and are certified to ISO 27001, ISO 9001, and NEN 4400-1.
Can we also build and manage AI models on Databricks?
Yes. With Mosaic AI and MLflow, you can code, train, and evaluate AI models yourself, and deploy the models within the same environment where your data already lives. That prevents exports to external tools and keeps your AI projects close to a reliable data foundation.
Do we have to switch to Databricks all at once?
No. We start with your biggest pain point and first build a Proof of Concept, so you concretely see what the platform delivers before you invest further. Only after that do we scale up step by step, with data management and access control in order from day one.