Embedded Files
Uses machine learning to:
Automatic provisioning
Automatic zero downtime patching
Encryption by default
Automatic threat detection
Automatic Tuning (index create/drop, memory tuning, query optimization/execution plans)
Automatic Resource Scaling (CPU/Storage) dynamically/automatically during workload spikes
Automatic Backups (for Point In Time Recovery)
Includes Partitioning, Advanced Compression, In-Memory
ATP (Transaction Processing)/ADW (Data Warehouse)/JSON variants
Pay As You Go (Autonomous is OPEX, On-Prem EE os CAPEX)
How the DBA Role changes
How the DBA Role changes
Moving to Oracle Autonomous Database doesn’t eliminate DBAs, it changes the role significantly from hands-on operators to higher-value engineers and advisors.
Data Architecture & Design
Data Architecture & Design
Data modelling (OLTP vs analytics structures)
Partitioning strategy
Schema design for performance and cost
Workload isolation (ATP vs ADW usage)
Performance Strategy
Performance Strategy
Analyse workload patterns
Identify inefficient application design
Guide developers on:
Bad joins / data access patterns
Over-fetching / chatty apps
Decide when to:
Split workloads
Use caching layers
Introduce data pipelines
Cost Management
Cost Management
Monitor CPU auto-scaling usage
Optimise queries to reduce consumption
Right-size workloads
Decide:
When to pause/resume DB
Storage vs compute trade-offs
Bad SQL now directly turns into cloud spend.
Data Governance & Security
Data Governance & Security
Data classification (PII, financial, etc.)
Access control design
Auditing & compliance
Data masking/tokenisation
Regulatory requirements (GDPR, etc.)
Integration & Data Pipelines
Integration & Data Pipelines
Design data movement pipelines
Work with:
OCI Data Integration
Streaming
ETL/ELT tools
Support:
Real-time analytics
Data lakes / lakehouses
API-based data access
DevOps / Platform Engineering
DevOps / Platform Engineering
Terraform / ARM / IaC for DB provisioning
CI/CD integration
Automation scripts
Environment consistency (dev/test/prod)
Troubleshooting Complex Issues
Troubleshooting Complex Issues
Investigate application-level problems
Work across layers (app + DB + cloud)
Diagnose:
Poor SQL patterns
Data skew
Concurrency issues
Vendor & Service Oversight
Vendor & Service Oversight
Monitor SLAs
Review Oracle recommendations
Validate automated decisions (indexes, scaling)
Escalate issues with Oracle support
Developer Enablement
Developer Enablement
Define standards
Create guardrails
Review designs early
Educate teams on:
Efficient SQL
Data access patterns
Most Autonomous DBAs evolve into one (or more) of these:
Data Platform Engineer
Owns the full data ecosystem
Automation + pipelines + architecture
Builder / architect mindset
Thinks in systems and flows
Works heavily with developers and analytics teams
Cloud DBA / FinOps Hybrid
Optimises performance and cost
Data Architect
Designs data structures, flows, and governance
DevOps Engineer (DB-focused)
Embeds database into CI/CD pipelines
DBRE
Database availability
Performance under load
Failover, HA/DR
Observability
SRE mindset applied to databases
Thinks in: SLAs / SLOs, Error budgets, Resilience
Heavy ops + automation focus
Create
Create
Oracle Database - Autonomous Transaction Processing
Bibliography
Bibliography
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