Gartner Research

Selecting SQL Engines for Modern Data Workloads

Published: 08 January 2020


Due to an increasing variety of data workloads, formats and types, it is increasingly difficult to select SQL engines. Data and analytics technical professionals must understand the limitations and choices for selecting SQL engines and architecting data processing solutions.

Included in Full Research

  • Why SQL for Large Data Stores?
  • Landscape of SQL Engines
  • Batch Workloads
    • Hive
    • Spark SQL
  • Interactive Workloads
    • Apache Impala
    • Apache Presto
    • Dremio
  • Streaming Workloads
    • Kafka SQL (KSQL)
    • Spark Structured Streaming SQL
    • Flink SQL
    • Druid
  • Operational Workloads — Introduction
    • Trafodion
    • Apache Phoenix SQL
    • Apache Kudu
  • Innovations
    • GPU-Based SQL Engines
    • PartiQL and SQL++
    • GQL (Graph Query Language)
    • SQL for ML
  • Strengths
  • Weaknesses
  • Framework for Evaluating SQL Engines
    • Prework


Sumit Pal

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