Description

This curriculum spans the breadth of operational database administration as practiced in medium-to-large enterprises, comparable in scope to a multi-workshop technical enablement program for DBAs supporting business-critical applications across hybrid environments.

Module 1: Database Architecture and System Selection

Evaluate trade-offs between OLTP and OLAP systems when designing transactional versus analytical workloads.
Select appropriate database engines (e.g., PostgreSQL, Oracle, SQL Server) based on licensing costs, feature sets, and organizational compliance requirements.
Determine replication topology (master-slave, multi-master, or logical) based on application consistency and availability needs.
Assess the impact of schema design choices (normalized vs. denormalized) on query performance and maintenance complexity.
Integrate time-series or JSON-optimized databases (e.g., TimescaleDB, MongoDB) only when relational models introduce excessive overhead.
Plan for hybrid deployments involving both on-premises and cloud-managed databases, including latency and data sovereignty implications.
Define data sharding strategies based on access patterns, growth projections, and query routing capabilities.
Validate that chosen database platforms support required encryption-at-rest and encryption-in-transit standards.

Module 2: Installation, Configuration, and Environment Management

Standardize database installation procedures using infrastructure-as-code tools (e.g., Ansible, Terraform) to ensure consistency across environments.
Configure memory allocation (shared buffers, cache sizes) based on host RAM and expected workload concurrency.
Set up environment-specific parameter files (e.g., postgresql.conf, my.cnf) with appropriate logging, connection, and timeout settings.
Implement role-based access for administrative tasks during setup to prevent overprivileged service accounts.
Isolate development, testing, and production instances using network segmentation and access controls.
Automate configuration drift detection using monitoring tools to maintain compliance with baseline settings.
Configure timezone, locale, and collation settings during initialization to avoid data comparison issues later.
Document and version-control all configuration changes to support audit and rollback procedures.

Module 3: Security, Access Control, and Compliance

Enforce principle of least privilege by assigning database roles based on job function rather than granting broad admin rights.
Implement row-level security policies to restrict data access within shared schemas based on user context.
Rotate database credentials and API keys using automated secret management (e.g., HashiCorp Vault, AWS Secrets Manager).
Configure audit logging to capture login attempts, DDL changes, and sensitive data access for compliance reporting.
Apply database firewall rules to block known malicious IPs and restrict access to approved application servers only.
Mask sensitive data in non-production environments using dynamic data masking or anonymization scripts.
Validate that all connections use TLS 1.2+ and disable outdated protocols like SSLv3.
Conduct quarterly access reviews to deactivate orphaned or excessive user accounts.

Module 4: Backup, Recovery, and Disaster Planning

Define RPO and RTO targets in collaboration with business stakeholders and align backup frequency accordingly.
Implement full, differential, and transaction log backups in a tiered schedule based on data volatility.
Test point-in-time recovery procedures quarterly using production-like data sets to validate recovery scripts.
Store backups in geographically separate locations with immutable storage options to prevent ransomware corruption.
Encrypt backup files using customer-managed keys and verify decryption during recovery drills.
Automate backup validation by restoring to a sandbox environment and running checksum comparisons.
Document failover and failback procedures for primary database outages, including DNS and connection string updates.
Coordinate with storage teams to ensure snapshot consistency across multi-disk database volumes.

Module 5: Performance Monitoring and Query Optimization

Deploy real-time monitoring tools (e.g., Prometheus, Datadog) to track query latency, lock contention, and connection pool usage.
Analyze slow query logs to identify and refactor inefficient SQL statements with missing indexes or full table scans.
Use execution plans to evaluate index effectiveness and avoid over-indexing on low-selectivity columns.
Implement connection pooling (e.g., PgBouncer, HikariCP) to reduce overhead from frequent connection establishment.
Set thresholds for long-running queries and configure automatic alerts or cancellations.
Optimize batch operations using bulk insert methods and appropriate transaction boundaries.
Monitor temp space usage to detect queries generating excessive spool or sort files.
Coordinate with application teams to eliminate N+1 query patterns in ORM-generated SQL.

Module 6: High Availability and Failover Management

Configure automatic failover using witness servers or quorum-based clustering (e.g., Always On AG, Patroni).
Test failover scenarios during maintenance windows to validate switchover time and data consistency.
Implement health checks that monitor replication lag and trigger alerts before thresholds are breached.
Use load balancers with health-aware routing to direct traffic away from degraded or offline nodes.
Document manual intervention steps for failover when automated systems are unresponsive.
Ensure DNS and connection strings support dynamic endpoint resolution after failover.
Validate that standby nodes are configured with identical parameter settings to prevent post-failover issues.
Monitor split-brain risks in multi-datacenter setups and use fencing mechanisms to prevent dual primaries.

Module 7: Patching, Upgrades, and Lifecycle Management

Develop a version support matrix aligned with vendor end-of-life dates and internal risk tolerance.
Test patches and minor version upgrades in staging environments before deployment to production.
Plan maintenance windows for upgrades, considering application downtime and rollback procedures.
Use blue-green deployment patterns for major version upgrades to minimize service interruption.
Validate compatibility of third-party tools (backup, monitoring, ETL) after database upgrades.
Preserve deprecated features temporarily during migration but enforce deprecation timelines.
Document known issues and workarounds associated with specific patch levels.
Coordinate with application teams to update drivers and connection libraries in sync with DB changes.

Module 8: Integration with Application and DevOps Ecosystems

Standardize SQL linting and schema migration tools (e.g., Liquibase, Flyway) across development teams.
Enforce pre-deployment schema review gates in CI/CD pipelines to prevent unapproved DDL changes.
Integrate database monitoring alerts into central incident management platforms (e.g., PagerDuty, Opsgenie).
Share performance baselines with development teams to inform query design and indexing strategies.
Support feature flag implementations by designing schema extensions that do not block rollbacks.
Implement canary deployments for schema changes using dual-write patterns and shadow tables.
Provide production data subsets for testing using data subsetting tools while maintaining referential integrity.
Collaborate with SRE teams to align database SLOs with overall service reliability metrics.

Module 9: Capacity Planning and Cost Optimization

Forecast storage growth using historical trends and adjust auto-extension settings proactively.
Right-size database instances based on CPU, memory, and IOPS utilization over time.
Archive cold data to lower-cost storage tiers using partitioning and retention policies.
Negotiate reserved instance pricing for stable production workloads in cloud environments.
Identify and decommission unused databases or schemas to reduce licensing and maintenance overhead.
Monitor index bloat and vacuum efficiency to reclaim disk space and improve performance.
Use query cost analysis to identify high-resource operations and optimize or throttle as needed.
Report on per-application database resource consumption to inform chargeback or showback models.