Hype Cycle for Data Management, 2018


Published: 25 July 2018 ID: G00340279

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Summary

This Hype Cycle helps data and analytics leaders interested in data management strategies to understand the evolutionary pace of maturing and emerging data management technologies. There is a noticeable lack of new technologies entering the Hype Cycle, while many are approaching the plateau.

Table of Contents

  • Analysis
    • What You Need to Know
    • The Hype Cycle
    • The Priority Matrix
    • Off the Hype Cycle
    • On the Rise
      • DataOps
      • Private Cloud dbPaaS
    • At the Peak
      • Machine Learning-Enabled Data Management
      • Data Classification
      • File Analysis
      • Time Series DBMS
      • Data as a Service
      • Data Catalog
      • SQL Interfaces to Cloud Object Stores
      • Event Stream Processing
      • In-Process HTAP
      • Blockchain
      • Multimodel DBMSs
    • Sliding Into the Trough
      • Data Preparation
      • Distributed Ledgers
      • Point-of-Decision HTAP
      • Data Lakes
      • Graph DBMSs
      • Spark
      • Information Stewardship Applications
      • Metadata Management Solutions
      • Cross-Platform Structured Data Archiving
      • Application Data Management
      • Key-Value DBMSs
      • Master Data Management
      • Wide-Column DBMSs
      • Operational In-Memory DBMS
      • Hadoop SQL Interfaces
      • iPaaS for Data Integration
      • In-DBMS Analytics
      • Logical Data Warehouse
    • Climbing the Slope
      • Document Store DBMSs
      • SaaS Archiving of Messaging Data
      • Data Integration Tools
      • Enterprise Information Archiving
      • Analytical In-Memory DBMS
      • Content Migration
    • Entering the Plateau
      • Database Encryption
      • Data Virtualization
      • In-Memory Data Grids
      • Database Platform as a Service
    • Appendixes
      • Hype Cycle Phases, Benefit Ratings and Maturity Levels
  • Gartner Recommended Reading
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