Data Lake Implementation. @data-lake-implementation

The Data Lake Implementation centralizes data a warehouse handles poorly — logs, events, documents, images, exports, high-volume raw feeds — into a governed cloud lake built on S3, ADLS, or GCS with open table formats such as Delta or Iceberg. The design follows the lakehouse pattern Scrums.com uses for enterprise data platforms: zoned storage from raw to curated, a catalog that makes data findable, and access control enforced at the data layer. The finish state is your defined sources landing reliably in the lake, discoverable through the catalog, and queryable by the analytics and ML workloads that needed this foundation.

The difference between a lake and a swamp is governance installed on day one — naming, ownership, schema management, and lifecycle rules — because retrofitting order onto a petabyte of mystery files is the project nobody survives.

Lake Architecture & Zones

Zoned design — raw, cleaned, curated — on your cloud's object storage with open table formats where transactions and schema evolution matter, plus lifecycle and cost policies per zone.

Ingestion & Format Standards

Batch and streaming ingestion for the defined sources, with format, partitioning, and naming standards enforced at write time so consistency is structural, not aspirational.

Catalog, Governance & Access

A data catalog with ownership and descriptions per dataset, schema registration, lineage signals, and role-based access with sensitive-data zones handled to your compliance requirements.

Consumption Interfaces

Query engines and connections for the people who need the data — SQL over the lake, warehouse external tables, notebooks and feature pipelines for ML — proven with real workloads before handover.

1. Scope — Fix the source inventory, zone and format standards, governance rules, and the first consuming workloads.
2. Build — Stand up storage, ingestion, and the catalog; land the sources and verify the consuming workloads run.
3. Handover — Production operation, governance documentation, and an onboarding pattern for adding new sources safely.

The Data Lake Implementation is a menu item on the Scrums.com delivery catalog, available at every plan tier. Add it to your plan backlog and your delivery team schedules it like any other item — scoped, tracked, and reported through the SEOP. See Delivery Plan Tiers.

Do we need a lake and a warehouse?

Different jobs: the lake holds everything cheaply in open formats, the warehouse serves modeled analytics fast. Lakehouse table formats blur the line, and the scope step will tell you honestly if one layer is enough for your case.

How do we keep it from becoming a swamp?

Standards enforced at write time, a catalog entry required for every dataset, and named ownership — the governance is mechanical, not a policy document. Unowned data does not get in.

What lands on top of the lake next?

Commonly modeled analytics via the Cloud Data Warehouse Implementation, or low-latency feeds via the Real-Time Data Streaming Pipeline — both menu items designed to compose with this one.