Gartner Research

Eventual Consistency and Its Implications: Can You Trust Your DBMS?

Published: 04 September 2015

ID: G00276734

Analyst(s): Rick Greenwald


Data consistency is the foundation of many business use cases; protecting that consistency is an essential responsibility of IT. Distributed DBMSs can allow data inconsistencies to appear. If a use case requires consistent data, you must understand the risks posed by eventual consistency approaches.

Table Of Contents



  • What Causes a Lack of Data Consistency?
  • How Do DBMSs Protect Data Consistency?
    • Consistency Issues
    • Consistency Tools
  • Why Is Data Consistency an Issue With Distributed DBMSs?
  • CAP Theorem
  • Consistency Versus Availability
  • Implications of Data Inconsistency
  • When Should You Consider Eventual Consistency?
  • Consistency in DBMSs
    • HBase
    • MapR
    • MongoDB
    • Cassandra
    • VoltDB
    • SQL Server
    • Oracle


  • Choose the DBMS That Can Meet Your Consistency Requirements and Delivers the Greatest Benefits
  • Determine the Consistency Requirements of Your Workloads, Now and in the Future
  • Match Consistency Requirements to Potential DBMS Offerings

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