Critical Capabilities for Data Management Solutions for Analytics

Published: 16 March 2018 ID: G00328243

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Data management solutions for analytics are continuing to improve, with key capabilities consolidating across all vendors. Real-time data analytics have moved to their own distinct use case. Data and analytics leaders can use this research to guide evaluation for modern DMSA offerings.

Table of Contents

  • What You Need to Know
  • Analysis
    • Critical Capabilities Use-Case Graphics
    • Vendors
      • Actian
      • Alibaba Cloud (MaxCompute)
      • Amazon Web Services (Amazon Redshift)
      • Cloudera (Cloudera Enterprise)
      • GBase (GBase 8a)
      • Google (BigQuery)
      • Hortonworks (Hortonworks Data Platform)
      • IBM (Db2)
      • MapR Technologies (Converged Data Platform)
      • MarkLogic
      • MemSQL
      • Micro Focus (Vertica)
      • Microsoft (SQL Server)
      • Neo4j
      • Oracle
      • Pivotal (Greenplum)
      • Qubole
      • SAP HANA
      • Snowflake
      • Teradata
      • Treasure Data
    • Context
    • Product/Service Class Definition
    • Critical Capabilities Definition
      • Access to Multiple Data Sources
      • Administration and Management
      • Advanced Analytics
      • Data Ingest
      • Managing Large Volumes of Data
      • Optimized Performance (Traditional)
      • Optimized Performance (Exploratory)
      • Flexible Scalability
      • Variety of Data Types
      • Workload Management
      • Traditional Use Support
      • Exploratory Use Support
    • Use Cases
      • Traditional Data Warehouse
      • Real-Time Data Warehouse
      • Logical Data Warehouse
      • Context-Independent Data Warehouse
    • Vendors Added and Dropped
      • Added
      • Dropped
  • Inclusion Criteria
    • Critical Capabilities Rating
  • Gartner Recommended Reading
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