Gartner Research

Hype Cycle for Data Management, 2019

Summary

This Hype Cycle will help data and analytics leaders interested in data management solutions to understand the evolutionary pace of maturing and emerging data management technologies. Most technologies have passed the Peak of Inflated Expectations, while many are approaching or on the plateau.

Published: 31 July 2019

ID: G00369950

Analyst(s): Donald Feinberg Adam Ronthal

Table Of Contents

Analysis

  • What You Need to Know
  • The Hype Cycle
  • The Priority Matrix
  • Off the Hype Cycle
  • On the Rise
    • DataOps
    • Ledger DBMS
    • Machine Learning-Enabled Data Quality
    • Private Cloud dbPaaS
  • At the Peak
    • Data Fabric
    • Data Hub Strategy
    • Data Catalog
    • Data Classification
    • File Analysis
    • Time Series DBMS
    • Augmented Transactions
    • Event Stream Processing
  • Sliding Into the Trough
    • Augmented Data Management
    • Data Preparation
    • SQL Interfaces to Cloud Object Stores
    • Multimodel DBMSs
    • Information Stewardship Applications
    • Metadata Management Solutions
    • Graph DBMSs
    • Application Data Management
    • Blockchain
    • Data Lakes
    • Distributed Ledgers
    • Spark
    • Master Data Management
  • Climbing the Slope
    • In-DBMS Analytics
    • iPaaS for Data Integration
    • SQL Interfaces to Hadoop
    • Logical Data Warehouse
    • Wide-Column DBMSs
    • Document Store DBMSs
    • Operational In-Memory DBMS
    • Data Integration Tools
    • Analytical In-Memory DBMS
  • Entering the Plateau
    • Content Migration
    • Database Encryption
    • Data Virtualization
    • In-Memory Data Grids
  • Appendixes
    • Hype Cycle Phases, Benefit Ratings and Maturity Levels

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