Gartner Research

Assessing Oracle Autonomous Database Capabilities

Published: 08 April 2020

ID: G00465267

Analyst(s): Ramke Ramakrishnan

Summary

Cloud databases have evolved with data collection and intelligent automation. This research shows data and analytics technical professionals how Oracle Autonomous Database is using applied machine learning to bring performance improvements and efficiency of scale with minimal human interaction.

Table Of Contents

Analysis

  • The Case for Autonomous DBMSs
  • Classification of Automation Using Bloom’s Taxonomy
  • Overview of Oracle Autonomous Database
  • Core Functions and Activities of Oracle Autonomous Database
  • Oracle Cloud Database Deployment Choices
    • Automated Models
    • Autonomous Models
    • Model Comparison
  • Oracle Autonomous Database Services
    • Oracle Autonomous Data Warehouse
    • Oracle Autonomous Transaction Processing
    • Comparison of Oracle Autonomous Database Services
  • Oracle Autonomous Database Deployment Types
    • Oracle ADB on Shared Infrastructure
    • Oracle ADB on Dedicated Infrastructure
  • Oracle Autonomous Database Life Cycle Management
    • Provisioning
    • Securing
    • Management
    • Protection
    • Scalability
    • Optimization
  • Strengths
  • Weaknesses

Guidance

  • Get Started With Oracle “Always Free” Resources
  • Decide How to Load Data Into Oracle Autonomous Database
  • Prepare Candidate Databases for Migration to Oracle Autonomous Database
  • Monitor the Autonomous Databases
  • Choose an Approach to Converged Database and Multimodel Support

The Details

  • Use of Machine Learning
    • Monitoring and Management
    • Application Development
  • Development and Administration Tools
    • Oracle Cloud Infrastructure Console
    • Oracle SQL Developer Web
    • Oracle Application Express
    • Oracle Enterprise Manager Cloud Control
  • Oracle Autonomous Health Framework
    • Diagnostics and Triage Management
    • Performance and Availability Management
  • Oracle Maximum Availability Architecture
  • Autonomous Health Cloud Platform
  • Use of Metrics and Machine Learning Algorithms
    • Use Case 1: Managing Database Hangs
    • Use Case 2: Preserving Database Availability
    • Use Case 3: Resolving Performance Degradation
    • Use Case 4: Preserving Node Availability
    • Use Case 5: Using Trace File Analyzer for Rapid Recovery
    • Use Case 6: Analyzing Anomalies to Discover and Detect Issues
    • Use Case 7: Identifying Maintenance Slots

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