Gartner Research

Selecting SQL Engines for Modern Data Workloads

Summary

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.

Published: 08 January 2020

ID: G00450999

Analyst(s): Sumit Pal

Table Of Contents

Analysis

  • 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

Guidance

  • Framework for Evaluating SQL Engines
    • Prework

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