RESEARCH PAPER: Autopilot Drives MySQL Database Service Differentiation

Time-to-value and time-to-action are two key metrics of success in the digitized economy, both measuring access to actionable intelligence. This intelligence is only useful when all data sources are appropriately transformed and analyzed.

MySQL, an open-source relational database, gained widespread popularity in companies of all sizes in the early 2000s. Today, MySQL instances are the back-end of many applications running across enterprises of all types, from content streaming providers to social media platforms to the world’s leading financial services companies. Countless invaluable data exists in these MySQL instances – data that, until recently, was difficult to aggregate holistically across a business.

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Table Of Contents

  • Executive Summary
  • HeatWave – An Introduction
  • The HeatWave Innovation Cycle
  • Innovation Grounded In The Real World
  • Autopilot – Automating Data Lifecycle Management
  • MySQL HeatWave Scale-Out Data Management
  • Scale-Out Across More Nodes
  • Adding It All Up – How MySQL Database Service With HeatWave And Autopilot Further Benefits The Data Consumer
  • In Closing
  • Figure 1: HeatWave Real-Time Analytics
  • Figure 2: HeatWave Price – Performance Leadership
  • Figure 3: MySQL Autopilot
  • Figure 4: Deep Analytics And ML Drive Automation In Autopilot
  • Figure 5: AutoProvisioning Process
  • Figure 6: Scale-Out Data Management In HeatWave
  • Figure 7: HeatWave V Snowflake

Companies Cited

  • GitHub
  • Oracle