Your Data Already Has the Answer. Nobody Can Reach It.

Sales lives in the CRM, costs in the ERP, traffic in Analytics, and the real numbers in a spreadsheet somebody rebuilds every Monday. We bring all of it into one modelled, queryable place, put dashboards on top that your team actually opens, and add forecasting or anomaly detection where a prediction beats a report. Open-source stack, no per-seat licences, and the platform stays yours.

What Our Big Data & Analytics Work Covers

• Data Audit and Source Inventory: Before building anything we map where your data actually lives: databases, SaaS APIs, exports nobody documented, and the spreadsheets that quietly became critical systems. You get an honest inventory of what you have, what contradicts itself, and what is missing to answer the questions that matter.
• Data Warehouse and Data Lake: One place where every source lands, modelled so a question has exactly one answer. We build on PostgreSQL or a columnar warehouse depending on your volume, with raw and modelled layers separated so a bad transformation never destroys the original data.
• Business Intelligence People Actually Open: Dashboards designed around the decisions your team makes each week, not around every metric we could plot. Each one answers a specific question, loads in under two seconds, and shows when the data was last refreshed so nobody has to ask whether the number is stale.
• Real-Time Streaming When It Earns Its Cost: Real time costs more to build and more to run, so we only use it where a delay has a price: stock that oversells, fraud that clears, a machine that fails. Everything else runs on scheduled batches, which are cheaper, simpler and easier to debug at three in the morning.
• Machine Learning on Your Own Data: Demand forecasting, customer segmentation, churn scoring, anomaly detection in operations. We start with the simplest model that beats your current guess, measure it against reality for a few weeks, and only add complexity if the numbers justify it.
• Governance and GDPR by Design: Access by role, personal data pseudonymised in the analytical layers, retention rules enforced by the pipeline instead of by a policy document, and a lineage trail from every dashboard figure back to its source. Built for European data protection from the first table, not patched in after an audit.

How a Data Project Runs

1. The Questions Before the Tools: We start from the decisions you want to make better, not from the technology. Five or six concrete questions ("which products lose money after shipping costs", "which customers are about to leave") define the whole scope and stop the project from becoming a warehouse nobody queries.
2. Source Audit and Feasibility: We connect to your systems and check whether the data needed to answer those questions actually exists, is complete, and agrees with itself. This is where we tell you honestly if a question cannot be answered yet and what you would have to start recording first.
3. Modelling and Metric Definitions: We write down, with your team, exactly how each metric is calculated and get it agreed before any dashboard exists. Most reporting disputes are definition disputes, and solving them at this stage costs a meeting instead of a rebuild.
4. Pipelines and Warehouse Build: Ingestion, raw storage, transformations and tests, deployed in Docker with the schedule and retries that each source needs. Everything is code in your repository, so nothing depends on a configuration somebody clicked into a tool.
5. Dashboards and Self-Service: We build the dashboards for the agreed questions and then train your team to answer the next ones without us. A data platform that needs a developer for every new chart has failed, however good the architecture is.
6. Monitoring, Quality and Handover: Freshness and volume checks on every table, alerts when a source stops sending or a number moves beyond its plausible range, and documentation good enough for your team or the next provider to take over. No hostage architectures.

How the Data Actually Moves

Every platform we build follows the same five layers. Knowing which layer a problem lives in is what makes a data stack maintainable instead of magic.

• Sources: Where the data is born: Production databases, ERP and CRM, SaaS APIs, event logs, IoT sensors, and the spreadsheets that still hold something nothing else does. Read-only connections: we never put the analytical load on the system that runs your business.
• Ingestion: Moving it without losing it: Scheduled extractions for most sources, change data capture where history matters, and event streams where latency has a cost. Every run is idempotent, so a failed job can be replayed without duplicating a single row.
• Storage: Raw first, always: The untouched copy lands first and is never overwritten. Modelled tables are rebuilt from it, which means a transformation bug costs you a re-run instead of a permanently corrupted history.
• Modelling: One metric, one definition: This is the layer that decides whether your company argues about numbers in meetings. Business logic lives here, in version control, tested: what counts as an active customer, when revenue is recognised, which orders are excluded.
• Consumption: Dashboards, alerts and models: BI for the recurring questions, alerts for the numbers that should interrupt someone, an API for the applications that need to query it, and machine learning where a prediction is worth more than a chart.

The Stack We Build Data Platforms On

Open-source and cloud-native components we run in production, chosen so your costs scale with data volume instead of with the number of people allowed to look at it.

Frequently Asked Questions

We are not a big company. Do we really have "big data"?

Probably not, and that is good news. The label matters far less than the problem: if your numbers live in five systems that disagree, or someone spends a full day each month rebuilding the same report, you have a data problem worth solving. The techniques are the same, the infrastructure is much cheaper, and a small company usually sees the benefit faster because there is less politics between the question and the answer.

Why build this instead of buying Power BI or Tableau?

Those are visualisation tools, and they are good ones. They do not solve the hard part, which is getting clean, agreed, connected data underneath them. Without that layer a BI licence just gives you prettier versions of the same contradictory numbers. We are happy to put Power BI on top of the platform we build if your team already knows it: the warehouse and the pipelines are what you are actually paying for.

How much does a data and analytics project cost?

A focused first platform (three to five sources, a modelled warehouse and two or three dashboards) typically runs from 8,000 to 20,000 euros. Adding real-time streaming or machine learning models pushes it higher, and a data audit on its own starts around 2,500 euros. The first consultation is free and ends with a fixed scope and price, not a range.

Where does our data live? Does it leave the EU?

You choose, and by default it stays in the EU. We deploy to European regions of AWS or Azure, to a European provider, or entirely on your own servers if the data is sensitive enough to justify it. Because the whole stack is open source, on-premise is a deployment decision rather than a different product.

Can you work with the systems we already have?

Yes, and we prefer it. We read from your existing ERP, CRM, e-commerce and custom applications without modifying them, using read-only access or replicas so the analytical workload never slows down production. If a system has no API, we work with database access, scheduled exports, or whatever it does offer.

What happens if we stop working with you?

You keep everything: the repository, the infrastructure definitions, the documentation and the data, all in your own accounts. We use standard open-source components precisely so that another team can pick it up. Handover documentation is part of the project, not an extra you negotiate at the end.