Gartner Research

Derive Value From Data Lakes Using Analytics Design Patterns

Published: 26 September 2017

ID: G00332852

Analyst(s): Svetlana Sicular , Joao Tapadinhas , Cindi Howson

Summary

Data and analytics leaders are feeling the pressure of rapidly increasing amounts of unprocessed data in data lakes. The challenge is in getting value from this data through analytics insights; this research outlines design patterns for implementing analytics on data lakes.

Table Of Contents
  • Key Challenges

Introduction

Analysis

  • Mix and Match Four Analytics Architecture Patterns to Extract Value From Data Lakes
    • Single-Tier Design Pattern — In-Data-Lake Analytics
    • Two-Tier Design Pattern — Abstracting Complexity of Data Lakes
    • Three-Tier Design Pattern — Extending Existing Business Analytics Tools to Data Lakes
    • Logical Data Warehouse Design Pattern — Comprehensive Analytic Solution
  • Map the Right Technologies to Implement the Design Patterns
    • Single-Tier Design Pattern: The Technology View
    • Two-Tier Design Pattern: The Technology View
    • Three-Tier Design Pattern: The Technology View
    • LDW Design Pattern: The Technology View
  • Accommodate Users' Skills and Preferences by Enabling Analytics on Data Lakes
    • Single-Tier Design Pattern: The User-Centric View
    • Two-Tier Design Pattern: The User-Centric View
    • Three-Tier Design Pattern: The User-Centric View
    • LDW Design Pattern: The User-Centric View
  • Use a Roadmap to Implement Design Patterns by Focusing on the Audience

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