Gartner Research

Modernize Your Data Integration Architecture for Digital Business by Combining Data Delivery Styles

Published: 19 September 2017

ID: G00327623

Analyst(s): Ted Friedman , Eric Thoo , Ehtisham Zaidi

Summary

Providing data optimally remains a major challenge for data and analytics leaders. Rather than stick with rigid data integration architectures, they should learn from the efforts of successful companies that are achieving agility and cost optimization by combining multiple data delivery styles.

Table Of Contents
  • Key Challenges

Introduction

Analysis

  • Combine Different Data Delivery Styles in a Portfolio-Based Approach That Includes Real-Time Data Flows, Variable Granularity and Data Virtualization
  • Learn From Real-World Examples of How to Combine Data Delivery Styles for Complex Use Cases
    • Scenario 1: Combination of Stream Data Integration and Bulk/Batch Data Integration for Stream Analytics
    • Scenario 2: Combination of Data Replication (Through CDC) and Data Virtualization for Enhanced Customer Experience
    • Scenario 3: Combination of Data Virtualization and Bulk/Batch Data Integration (Through ETL) for IoT Data Integration
    • Scenario 4: Combination of Bulk/Batch Integration (Through ETL) and Data Replication (Through CDC) for Cloud Data Movement and Logical Data Warehousing
    • Scenario 5: Combination of Data Replication (Through CDC) and Message-Oriented Data Movement (for Application Integration) to Enable an Integrated Digital Business
  • Recommendations

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