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DataLake Architect / Architecture & IntegrationGENEDATA / 01

Make storage a usable data foundation.

Defines storage layout, table and lifecycle patterns, interoperability, recovery, governance, and lakehouse evolution.

Talk to the teamYour workflow
Shared context · Lineage · Governance
Connected
Your sources
Design the approach
Model shared meaning
Apply governance
Connected intelligenceDataLake Architect
Governance
Business impactOptimize cost and performance
Shared contextLineageGovernance
+Illustrative workflow01 / 03
1 / 3
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Role context

The context behind the work.

You organize the storage foundation so that data remains discoverable and useful throughout its lifecycle. Layout, partitioning, retention, access, and recovery choices should reflect how data is produced and consumed, rather than a folder convention alone.

In practice / 01

Create a shared artifact and dataset layout

Inventory the producers and consumers, agree naming and ownership conventions, and review expected access patterns. Validate a representative write and retrieval workflow before making the layout a shared standard.

In practice / 02

Review retained data and archive behavior

Connect the retained assets to their owners and lifecycle requirements. Test whether the required artifacts can be retrieved and understood, then document gaps in retention or recovery procedures.

Your workflow

A practical path from task to outcome.

Design a data-lake layout that supports discovery, retention, and recovery.

  1. 01

    Design the approach

    Inventory the data domains, artifact types, consumers, and lifecycle requirements.

  2. 02

    Model shared meaning

    Define a consistent storage layout, naming scheme, partition strategy, and ownership model.

  3. 03

    Apply governance

    Review retention, archival, access, and recovery procedures with governance and operations.

  4. 04

    Optimize cost and performance

    Publish the design and validate a representative ingestion and retrieval workflow before expanding it.

What you take forward

A storage design with lifecycle rules, ownership, and recovery evidence.

Work more effectively

Less repeated effort. More useful work.

Explore the habits and platform connections that can make this role easier, more consistent, and easier to collaborate with.

A common friction

Inconsistent layouts across storage domains

Provide reusable naming and partitioning conventions.

A common friction

Data retained without a clear lifecycle

Connect storage decisions to documented retention and ownership requirements.

A common friction

Recovery assumptions that remain untested

Include a representative retrieval and recovery exercise in the design review.

Measure your own improvement

Choose a baseline before you begin. Review these signals with your team; results depend on your data, process, and implementation.

  • Time to locate the correct dataset or artifact
  • Storage domains with reviewed lifecycle and recovery rules
Get started

Build confidence with a first task.

Design a data-lake layout that supports discovery, retention, and recovery.

Use AI with judgment

Use AI to summarize layout alternatives; validate cost, access, and retrieval behavior against the real workload.

Your practice checklist

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Your toolkit

The right surfaces. The right people.

Continue into the product, deepen your knowledge, or follow the next role in the handoff.

Go deeper

Technical workbookDocumentation

Technical workbooks are maintained in English. Workspace access and available capabilities depend on your deployment and permissions.

DataLake Architect

Bring your own workflow.

Explore how these practices could fit your team, your data, and your operating requirements.

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