Glossary
What is data lake?
A data lake is a store for large volumes of raw data in any format, such as tables, files and logs. You only define the structure when the data is read.
Data lake explained
A data lake usually runs on low-cost cloud object storage, such as Azure Data Lake Storage or Amazon S3. You store files as they are: CSV, JSON, Parquet, log files or images. This principle is called schema-on-read: whoever uses the data decides how it is read.
That makes a lake flexible and suited to large volumes, data science and unstructured data. The risk is that it turns into a swamp: lots of files without a clear owner, version or meaning, which nobody dares to rely on any more.
For standard reporting and KPIs you therefore usually still need a structured layer, such as a data warehouse or lakehouse. A lake does not replace that layer, but often serves as storage for raw source data.
Example
Acme Groothandel keeps the raw export files from its web shop and the sensor data from its warehouse in a data lake. For the monthly management dashboard, only the checked sales figures are moved from there into the data warehouse.
Data lake in ENABLE
ENABLE does not use a data lake as a source: the SQL Explorer, the Data API and the Dashboard Builder (pilot) read from the customer's PostgreSQL data warehouse. Data from a lake can, however, be loaded into that warehouse outside the portal.
FAQ
Frequently asked questions
What is the difference between a data lake and a data warehouse?
A data lake stores raw data in any format, while a data warehouse stores structured, cleaned tables for reporting. A lake is more flexible; a warehouse is more reliable for fixed figures.
When do you need a data lake?
When you have large volumes or many kinds of data, such as log files, sensor data or documents, and for data science. For standard reporting on ERP and CRM data, a data warehouse is often enough.
What is a data swamp?
A data swamp is a data lake that has become unusable because nobody knows what data it holds, where it came from or whether it is correct. Clear ownership and a data catalogue help prevent it.
Further reading
- ResourcesWhat is a data warehouse? Explained and comparedA data warehouse is a central database in which you bring together data from different source systems, such as your ERP, CRM and webshop, clean it up and organise it so that you can report on it and analyse it effectively. A source system is built to process transactions; a data warehouse is built for reading, comparing and keeping history. A data lake stores raw data in any format, and a data lakehouse combines the storage of a lake with the structure of a warehouse.Read more
- SolutionBuild a data platform: layers, choices and approachFrom sources to dashboards and AI: how a data platform is structured.Read more
- SolutionMicrosoft Fabric: what it is and when it fitsWhat Microsoft Fabric is, when it fits and how it relates to ENABLE.Read more
- ResourcesGlossary of data, AI and Power BI termsWhat is a service principal, row-level security, a semantic model, text-to-SQL or RAG? Short, clear definitions of terms around data, Power BI and AI.Read more
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