SCNET · Enterprise IT · Ankara, Türkiye
A data lake is not about copying everything into one place. Sanal Çekirdek builds it as layers: raw data stays as it arrived, the cleansed layer is trustworthy, and the serving layer is ready for use.
Data without a catalog and an owner eventually becomes a pile nobody trusts. Producing reports from that pile is riskier than producing none.
Raw, cleansed and serving layers stay separate. The raw layer is a copy of the source and is never modified; corrections happen in the cleansed layer so the origin of an error remains traceable.
Every dataset needs an owner, a definition and a quality expectation. The catalog gives that information to whoever is searching, and quality rules turn the expectation into something measurable.
A lake holds the broadest collection of data in the organization, so access design is part of the architecture. Fields containing personal data are marked, and masking and aggregation are applied through a rule set.
Storage is cheap; querying is not. Unless partitioning, file format and retention tiers are planned early, cost grows faster than data volume — and unused datasets live forever.
It complements rather than replaces. A warehouse is efficient for structured, frequently queried data; a lake holds raw and varied data. In most organizations the two run together.
No. Data with no use case brings nothing but cost and compliance burden. Source selection works backwards from the use case.
Catalog, ownership and quality rules. Without those three the lake works technically but becomes a store nobody trusts.
Share your use cases and we will decide together which sources belong in the lake.
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