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Data Warehouse vs Data Lake vs Data Mesh
Storing data is the easy part. Deciding where and how to organize it is the real challenge.
A data warehouse is the traditional approach. It cleans and structures data before storing it. Queries run fast, and reports stay consistent. But adding a new data source takes effort because everything has to fit the schema first.
A data lake takes the opposite approach. It stores everything raw, like databases, logs, images, and video. Process it when you need it. The flexibility is great, but if rules around naming, formatting, and ownership are not properly set, you end up with duplicate, outdated, and undocumented data that is hard to manage.
Data mesh shifts data ownership from a central team to individual departments. For example, sales publishes sales data, and finance publishes finance data. Shared standards keep things compatible across teams.
It works well in larger organizations. But it requires every team to have the right people and processes to manage their data quality, documentation, and access, which is a challenge.
In practice, many companies use more than one approach. They'll use a warehouse for dashboards and reporting, a lake for machine learning workloads and start applying mesh principles as teams scale.
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