“Do we need a data warehouse, a data lake, or both?” Your IT director or your provider may already have asked you this. A third answer now comes up in almost every architecture discussion: the data lakehouse.
Behind this portmanteau lies a choice that commits your costs, your analysis tools and your future AI projects. You do not need to master the technology. You do, however, need to understand what you are buying, and why.
Warehouse, lake, lakehouse: three storage logics
A data warehouse is a database organised in tables, designed to produce reliable reports. It stores structured data: sales, invoices, stock. It answers quickly, but handles documents, images and technical logs poorly.
A data lake is a storage space that keeps raw files of every format at low cost. It accepts everything, but imposes no order. Without discipline, it becomes a “swamp” where nobody finds anything.
A lakehouse is an architecture that stores all data in one place, like a lake, and queries it with the rigour of a warehouse. A software layer adds what raw storage lacked: tables, a catalogue, access rights and a change history.
What this choice changes in your decisions
In many companies, the same data exists in several copies: one for accounting, one for marketing, one for the AI project. Each copy evolves on its own and the figures diverge. The lakehouse aims for a single reference copy that all tools consult.
For a manager, three consequences matter:
- the finance director’s dashboard and the forecasting model start from the same figures;
- you pay for one storage system instead of maintaining two systems and the flows that link them;
- open formats, readable by several market tools, limit your dependence on a single vendor.
These benefits depend on implementation. A well-designed architecture does not correct poorly entered data. It makes their cleaning and control easier to organise, which is the foundation of a serious artificial intelligence and BI project.
Why AI needs it
Dashboards consume neatly organised figures. AI also consumes the rest: emails, contracts, site photos, sensor readings, customer service conversations. A classic warehouse discards this content. A lake keeps it without control. The lakehouse keeps it and places it under the same rules as the figures.
This point determines reliability. An AI assistant that draws on outdated documents or contradictory tables gives wrong answers with confidence. With clean, accessible, governed data, you know where each answer comes from and who is allowed to see it.
Take a manufacturer of mechanical parts. Its production orders live in the management software, machine readings in technical files, and customer complaints in email. As long as these three sources stay separate, no model can link a machine setting to a defect a customer reports.
Do you need a lakehouse? A method to decide
Not every company needs one. Start by asking yourself four questions:
- Must your analyses combine figures with unstructured content, such as documents or images?
- Do you already maintain several copies of the same data for different uses?
- Do you plan AI projects that rely on your own data?
- Are your volumes growing to the point of making your current database slow or costly?
If you answer no to all of them, a well-maintained database and a BI tool are enough. If you answer yes to two or more, proceed in stages:
- Choose one specific use case, for example margin tracking by product family.
- Load the relevant raw data into cloud storage, without transforming it.
- Organise three levels: raw data, cleaned data, data ready for analysis.
- Describe each table in a catalogue: content, owner, update frequency.
- Connect a first dashboard, then a first model, to the level ready for analysis.
Cloud services billed on usage make this path accessible to an SME. You start with a limited scope and extend the architecture once the first use case proves its value.
Limits and pitfalls to know
- A lakehouse does not replace governance. Without designated owners or quality rules, you get a more modern swamp.
- The technology requires skills. Plan for a partner or training for the team that runs it.
- The cloud bill climbs when nobody monitors it. Set budgets and alerts from the start.
- Vendor marketing blurs the boundaries. Ask each one what it stores, in which format, and how you recover your data if you leave.
- A small organisation with few sources rarely gains by adopting this architecture too early.
What changes by sector
- Manufacturing: you bring together machine readings, production orders and quality checks. A model then detects drifts before a breakdown or scrap.
- Multi-store retail: you match till receipts, stock and promotional operations. AI forecasts demand by store from a common base.
- Energy and utilities: you keep long series of consumption measurements at low cost. Teams anticipate peaks and spot anomalies on the network.
The AISYSNEXT approach
AISYSNEXT designs data architectures sized to the company. Our artificial intelligence and BI team first assesses your sources, uses and volumes. It then recommends the simplest solution that meets the need, lakehouse or not.
Torn between several architectures? Contact our expert team for a first discussion of your situation and priorities.
Frequently asked questions
What is the difference between a data lake and a data lakehouse?
A data lake stores raw files without imposing a structure. The lakehouse adds tables, a catalogue, access rights and a history on top. You keep the low cost of the lake and gain the reliability of a warehouse.
Does a lakehouse replace my ERP?
No. The ERP manages your daily operations: orders, invoices, stock. The lakehouse receives a copy of this data and combines it with other sources for analysis and AI.
Can an SME do without a lakehouse?
Yes, in many cases. A well-structured database and a BI tool cover the needs as long as the sources remain few. The lakehouse becomes relevant when volumes, content variety or AI projects grow.
