Gartner Research

Hype Cycle for Data Management, 2017

Published: 26 July 2017

ID: G00313950

Analyst(s): Adam Ronthal , Donald Feinberg

Summary

This Hype Cycle helps CIOs and data and analytics leaders understand the evolutionary pace of maturing and emerging data management technologies and challenges. It will create awareness of novel technologies as well as the opportunities and risks in the nascent areas.

Table Of Contents

Analysis

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

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