Gartner Research

Three Architecture Styles for a Useful Data Lake

Published: 15 July 2016

ID: G00303817

Analyst(s): Svetlana Sicular


A data lake extends processing and analytics capabilities to unrefined data in its native or near-native format. Implementers of big data solutions should select a proper data lake architecture to derive the most value from this data store.

Table Of Contents


  • Gartner's Definition of a Data Lake
    • Data Lake Architecture Styles
    • Comparison of the Data Lake Architecture Styles


  • Differentiating Data Lake Architecture Styles
    • Architecture Style No. 1: An Inflow Data Lake
    • Architecture Style No. 2: An Outflow Data Lake
    • Architecture Style No. 3: A Data Science Lab Data Lake
  • Summary of the Data Lake Architecture Styles
  • Future of the Data Lake Architectures: Separation of Storage and Compute
  • Ins and Outs of a Data Lake
  • Common Success Factors for Building Data Lakes
    • Selecting Appropriate Underlying Technologies
    • Optimizing the Supporting Infrastructure
    • Acquiring Metadata
    • Applying Data Modeling
    • Establishing Data Governance
    • Obtaining the Right Skills


  • Start With the Single Architecture Style
  • Implement the Data Lake for Its New Capabilities
  • Carefully Plan How the Data Flows In and Out of the Lake
    • Data Ingest
    • Data Consumption
  • Beware of Overcommitting and Underdelivering

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