This course provided a solid foundation in relational database administration using MySQL, Db2, and PostgreSQL. It focused on the lifecycle of a database, how to manage it effectively, monitor performance, and secure access. It’s not a track I expect to specialize in, but it was helpful to learn the basics and build some confidence for future troubleshooting or system-level data work

Module 1: Introduction to Database Management

This module introduced the core responsibilities of a DBA and walked through the lifecycle of managing relational databases.

Key tasks of a DBA:

Designing and planning databases
Managing server and DB objects
Setting up backup and recovery procedures
Handling user access and security
Monitoring and optimizing performance
Automating routine tasks

Database lifecycle phases:

Requirement analysis – define scope, users, and purpose
Design and planning – schema modeling, ERDs, access control
Implementation – build objects, configure access, set up automation
Monitoring & maintenance – patching, upgrades, log review, compliance

Important concepts:

System catalogs: store DB metadata and can be queried
Instances → Databases → Schemas → Objects: the standard hierarchy
Tablespaces: logical storage containers mapped to physical containers
Storage types: hot/cold data, partitioning, storage groups
MySQL storage engines: e.g. InnoDB, MyISAM, MEMORY, ARCHIVE

Module 2: Managing Databases

This one was about backup strategies, recovery techniques, and system-level security and encryption.

Backup types:

Full – complete snapshot
Differential – changes since last full
Incremental – changes since last backup (full or incremental)
Point-in-time recovery – restores DB to a specific moment using logs

Physical vs. logical backups:

Physical – faster, file-level, less flexible
Logical – SQL-based, granular, slower

Backup policies include:

Hot (online) vs. Cold (offline)
Compression, encryption, frequency, scheduling
Cloud options: automated or manual

Security layers:

Authentication – identity verification
Authorization – permissions
Auditing – tracks access and actions
Encryption – at rest and in transit
Roles & groups – simplify access management

Encryption types:

Symmetric – faster, shared key (e.g. AES)
Asymmetric – public/private keys (e.g. RSA)
Transparent Data Encryption – automatic full-DB encryption

Module 3: Monitoring and Optimization

This module focused on keeping a database healthy, responsive, and efficient over time.

Monitoring strategies:

Reactive – respond after problems occur
Proactive – detect anomalies early using alerts and baselines

Key metrics:

Throughput (queries/sec)
Response time, availability, resource usage
Contention, lock waits, buffer pool usage

Tools:

MySQL: Workbench, Query Profiler
PostgreSQL: pgAdmin, pganalyze
Db2: Data Management Console, Snapshot monitors
3rd-party: Datadog, SolarWinds, PRTG

Optimization techniques:

MySQL: OPTIMIZE TABLE
PostgreSQL: VACUUM, REINDEX, ANALYZE
Db2: RUNSTATS, REORG

Query tuning:

Use EXPLAIN plans to understand execution
Avoid SELECT *, use indexes, rewrite slow queries
Consider buffer sizes and schema design
Use tools like EverSQL, dbForge, shell benchmarking

Indexing:

Boosts read speed, slows down writes
Choose columns based on usage (WHERE, JOIN, ORDER BY)
Types: Primary, Unique, Full-text, Prefix
Poor indexing = common bottleneck

Module 4: Troubleshooting and Automation

Final module focused on system reliability, error investigation, and reducing manual work through scripts and alerts.

Troubleshooting:

Ask the right questions (what, where, when, conditions, reproducibility)
Check logs: error logs, trace logs, event logs
Use platform tools (e.g. SHOW STATUS, db2pd, pg_isready)
Error codes + documentation are essential

Logs contain:

Severity, timestamp, user, event description
Usually plain text or tool-readable formats
Need to be checked frequently

Automation:

Automate time-consuming, repetitive tasks
Use scripts for backup, cleanup, health checks
Schedule with cron jobs or external tools
Keep everything under version control

Alerting and reports:

Reports = system health over time
Alerts = real-time problems (e.g. failed jobs, low disk space)
Thresholds: warning vs. critical
Customize content and frequency

Final Thoughts

While I don’t expect to work as a full-time DBA, this course gave me a much clearer understanding of:

How relational databases are structured
What it takes to maintain their performance and integrity
How to back them up, secure them, and tune them for better speed

It’s a useful foundation for anyone working in data engineering, backend infrastructure, or even analytics roles that need to interact with production databases

Course Certificate: View on Coursera

All notes and opinions are personal interpretations of the IBM Python for Data Science, AI & Development course on Coursera.

IBM Data Engineering: Relational Database Administration