Learning

• Analytics Accelerator learning resources
  • Documentation categories
    • Structured learning paths
    • Conceptual explanations
    • How-to guides
    • Use cases and solutions
    • Persona-based guidance
    • Analytics Accelerator in Hybrid Manager (HM)
  • Next steps
• Analytics Accelerator Learning Paths
  • How to use these learning paths
  • Learning path levels
    • Level 101: Foundations of the Analytics Accelerator
    • Level 201: Applying the Analytics Accelerator — core technologies and use cases
    • Level 301: Advanced Analytics Accelerator, optimization, and architecture
  • EDB training and certification
  • Additional guidance
  • Next steps
• Analytics Accelerator 101 Foundational concepts
  • What you will learn
  • Before you begin
  • Suggested learning path
  • Next steps
• Analytics Accelerator 201 Practical application and core solutions
  • What you will learn
  • Before you begin
  • Suggested learning path
  • Next steps
• Analytics Accelerator 301 Advanced techniques and optimization
  • What you will learn
  • Before you begin
  • Suggested learning path
  • Next steps
• Analytics Accelerator for your role a persona-based guide
  • Quickly find your role
  • Database administrator (DBA)
    • Key objectives and Analytics Accelerator solutions
    • Performance monitoring and tuning
    • Storage management and cost optimization
    • Capacity planning
    • Backup and recovery of analytical data
    • Ensuring data accessibility for analytics
    • Recommended resources
  • DevOps engineer / site reliability engineer (SRE)
    • Key objectives and Analytics Accelerator solutions
    • Automated provisioning and management
    • Scalability and elasticity
    • Monitoring and observability
    • CI/CD for data pipelines and analytical applications
    • Cost management for cloud resources
    • Recommended resources
  • Data scientist / data analyst
    • Key objectives and Analytics Accelerator solutions
    • Accessing large and diverse datasets
    • Data preparation and exploration
    • Working with open table formats
    • Integration with data science tools and frameworks
    • Query performance
    • Recommended resources
  • Application developer
    • Key objectives and Analytics Accelerator solutions
    • Building applications with analytical features
    • Efficiently handling large reporting queries
    • Accessing data from a unified platform
    • Developing AI/ML-enabled applications
    • Recommended resources