PostgreSQL Administration
Own the database everything depends on. Tune it, replicate it, secure it, and restore it under pressure — on the engine that now runs everything from transactions to vector search. The data layer of the platform track: the one component you cannot simply redeploy when it breaks.
This is the data layer of the platform track. Linux is the host, containers are the runtime — and this is where the state lives: the one component you cannot simply redeploy when it breaks. PostgreSQL has become the default open-source relational database for new applications, and its extension ecosystem has carried that position into geospatial, time-series and now vector and AI workloads. One skill set now covers a widening range of systems — and this course is about operating it properly, not just querying it.
Anyone Can Install It. The Job Is Keeping It Alive.
Free PostgreSQL content is abundant and teaches SQL and basic configuration. What it does not teach is what actually takes databases down. So we build this course around the hard part:
Anyone can install PostgreSQL. The job is keeping it fast as the data grows, keeping it up when a node dies, and getting the data back when someone drops the wrong table — with the business waiting.
Database mistakes are the hardest in software to reverse, so recovery is not a final chapter here — it is the spine. You will corrupt, fail over and restore real clusters, and the operational failure modes that cause real incidents are foregrounded, not footnoted:
- Autovacuum, bloat and transaction ID wraparound — the most characteristic PostgreSQL operational failure, and the one most teams discover the hard way
- Connection handling and pooling — the architecture makes connections expensive, so pooling is not optional at scale; this surprises people arriving from other databases
- Locking and migrations under load — how a routine schema change stops an application dead, and the patterns that avoid it
- Backup verification and point-in-time recovery — an untested backup is not a backup; restore drills are a graded exercise here
- Reading query plans — the primary diagnostic skill, taught as a method rather than guessing at indexes
AI Writes SQL Fluently — And Ships the Mistakes That Kill Databases
AI writes SQL and schema definitions fluently, and produces exactly the things that destroy a production database later: missing or redundant indexes, migrations that take an exclusive lock on a large table, queries that look fine on ten rows and collapse on ten million, connection handling that exhausts the server, and schema choices that cannot be undone once there is data in them. Database mistakes are the hardest in software to reverse.
The value this course builds is judgment about locking, plans, data volume and recovery — precisely what an assistant cannot supply, because it cannot see your data distribution, your lock waits or your recovery objectives. There is a short, practical block on working effectively with AI tooling without shipping its mistakes: how to read what it proposes against an execution plan, a lock analysis and the actual size of your tables before it reaches production.
A 2026 Syllabus, Not a 2016 One
PostgreSQL is now the default database for AI applications. The extension ecosystem — particularly vector similarity search with pgvector — lets teams keep operational data and embeddings in one system instead of running a separate vector database. This course gives that real teaching time: vector index types, index build cost and memory, the recall-versus-latency trade-off, hybrid search combining full-text and vector similarity, and the operational implications of adding this workload to a transactional database. It connects directly to our Generative AI & LLM Engineering, AI Agent Development and Prompt Engineering courses — which build the retrieval systems this database now backs.
The current major release changed operational practice, and we teach it with the nuance most write-ups skip. The new asynchronous I/O subsystem improves reads and maintenance operations — sequential scans, bitmap heap scans and vacuum — so a write-heavy transactional workload should not be promised a step change; understanding which workloads benefit is more valuable than any headline number. Planner statistics now survive a major-version upgrade, which materially lowers upgrade risk and post-upgrade performance surprises (with the caveat that extended statistics still need refreshing). Skip scan widens where multicolumn indexes are useful; timestamp-ordered UUID generation addresses why random UUID primary keys hurt index locality; virtual generated columns, temporal/range constraints and token-based authentication are covered; and data checksums are now enabled by default for new databases — including the upgrade implication that checksum settings must match.
And licence cost is driving sustained migration off commercial databases — Oracle, SQL Server and others — into PostgreSQL, especially in enterprise and public-sector environments. We cover assessment, schema and code conversion, the semantic differences that break naïve migrations, and data-movement and cutover strategy, because this is funded, deadline-driven work.
Course at a Glance
- Duration: 9 weeks | ~65 hours | live + hands-on
- Format: Live online · lab-heavy · you will corrupt, fail over and restore real clusters
- Prerequisite: Basic SQL and comfort on the Linux command line. No prior database administration experience required.
- Curriculum: 10 modules, from the architecture to a defended production cluster
- Certification: Aligned to the recognised vendor certification track; the field hires primarily on demonstrated operational ability
- Curriculum reviewed: July 2026 — the current major release changed operational practice; this syllabus is checked against the official PostgreSQL documentation
Course Highlights
- Every module leads with what you configure, tune, diagnose or recover — not what you "learn about"
- Recovery as the spine — graded restore drills with measured recovery objectives
- The failure modes nobody teaches — wraparound, bloat, lock storms, connection exhaustion
- Execution plans taught as a method, not a guessing game
- Schema migrations that don't lock a production table under live load
- Rehearsed failover and a real major-version upgrade via logical replication
- Vector search workloads — index tuning and hybrid retrieval alongside relational data; barely covered in any DBA course
- A migration-off-commercial-database assessment and cutover plan
Where This Course Sits in the Track
This course is scoped as the server side of the data layer, and it stays in its lane:
- Snowflake Data Warehouse owns analytics at warehouse scale — a different workload. This course owns operational / transactional databases. They are complementary, not competing. See our Snowflake Data Warehouse course for the analytics side.
- Docker & Kubernetes covers running stateful workloads on clusters conceptually; here we go deep on the database side and give an honest view of when running a database on Kubernetes is — and isn't — a good idea. See our Docker & Kubernetes course for orchestration.
- Application courses cover ORMs and data access from the application side; this is the server side — administration, performance, availability and recovery. See Django or FastAPI for the application data layer.
- The host underneath it is our Linux System Administration course — storage, systemd and the operating system tuning a database depends on.
Hands-on Projects & Portfolio
From scratch, with a justified configuration for a given workload.
Cut a slow application's worst queries substantially, with execution-plan evidence before and after.
Create the problem deliberately, detect it, remediate it without downtime.
Apply schema changes to a large table under live load.
Sizing justified by measurement, not by guesswork.
Point-in-time restore to a moment just before a destructive change, with a measured recovery time.
Automated failover, then a rehearsed failover under load.
Perform a major-version upgrade with minimal downtime.
Index tuning and hybrid retrieval alongside relational data.
Convert a schema and produce a cutover plan.
A production-grade cluster — tuned, replicated, secured, monitored and defended.
Tools & Stack
PostgreSQL psql pgAdmin pg_stat_statements pgBouncer pgBackRest Patroni pgvector PostGIS Citus pg_dump / pg_restore pg_upgrade EXPLAIN / ANALYZE pgbench Prometheus Grafana postgres_exporter Ansible Docker Kubernetes operators AWS RDS & Aurora Cloud SQL Azure Database for PostgreSQL Linux
Certification Alignment
- Aligned to the recognised vendor-backed PostgreSQL certification track from EDB (EnterpriseDB), which offers tiered associate, professional and master-level credentials for production database administration
- An honest note: this field hires primarily on demonstrated operational ability — a cluster you have tuned, replicated, broken and recovered speaks louder than any certificate, which is why this course produces exactly that evidence
- Presented as preparation and alignment only — no official partnership or accreditation implied, and no exam outcome guaranteed
Career Outcomes
PostgreSQL has become the default open-source relational database for new applications, and its extension ecosystem has extended that position into geospatial, time-series and now vector and AI workloads — so one skill set covers a widening range of systems. Licence costs are driving sustained migration from proprietary databases, creating steady demand for people who can plan and execute those moves. And database expertise is unusually durable: the fundamentals of storage, concurrency, locking and recovery outlast any framework cycle. This course targets:
- Database Administrator
- PostgreSQL DBA
- Database Reliability Engineer
- Data Platform Engineer
- DevOps or SRE with database ownership
- Backend Engineer owning the data layer
- Cloud Database Engineer
- Migration Consultant
Who This Course Is For
- Final-year students and fresh IT graduates targeting database or platform roles
- Backend developers who own a production database but were never taught to operate one — the largest and most common gap this course closes
- DevOps, SRE and platform engineers inheriting database responsibility
- Oracle, SQL Server and other commercial database professionals migrating to open source — funded, deadline-driven work
- Data engineers who need operational depth beyond writing queries
- AI and application engineers running vector search on PostgreSQL who need to operate it properly
- Anyone who has restored from a backup for the first time during an actual incident
Material Includes
- Class recordings
- Multi-node cluster lab & deliberately broken practice databases
- Runbooks, tuning checklists & recovery drill templates
- Certificate
Prerequisites
- Basic SQL
- Comfort on the Linux command line
- No prior database administration experience required
Estimated Time: 6 Hours
You configure:- A cluster from the ground up — and, more importantly, build the mental model that makes every later decision about memory, locking and recovery obvious
- The process model and why connections are expensive; shared buffers and the operating system page cache; the write-ahead log; checkpoints
- Multiversion concurrency control and what it implies for storage; transaction isolation levels in practice; the system catalogs
- Installation and cluster layout
Estimated Time: 6 Hours
You configure:- A sane baseline for a new server — memory, logging, roles and extensions set deliberately, not copied from a blog
- The configuration files; reload-versus-restart parameters; memory settings that matter and the ones people copy blindly
- Logging configuration for diagnosis rather than noise; extensions; roles and databases
- A sane baseline for a new server
Estimated Time: 7 Hours
You configure:- Schema migrations that don't lock a production table — the change patterns that keep an application online while the structure underneath it changes
- Data types including JSON and arrays used well; constraints; primary key strategy and why random UUIDs hurt index locality; generated and temporal/range constraint features
- Indexing strategies across the available index types; partial and expression indexes; partitioning
- Schema migrations that don't lock a production table
Estimated Time: 7 Hours
You diagnose:- A slow query taken apart with a systematic method — reading the plan, fixing the cause, and proving the improvement with evidence rather than folklore
- Reading execution plans properly; planner statistics and how they go wrong; index selection and skip scan; join strategies
- Common inefficient patterns; query rewriting; extended statistics
- Tracking slow queries with the standard statistics extension; a systematic method for taking a slow query apart
Estimated Time: 7 Hours
You diagnose:- Transaction ID wraparound before it becomes an emergency — and remediate table and index bloat without taking the application down
- How multiversion concurrency creates dead tuples; autovacuum tuning for real workloads; table and index bloat detection and remediation
- Transaction ID wraparound and how to avoid the emergency; reindexing without downtime; analyse and statistics maintenance
- Long-running transactions as the root of most maintenance problems
Estimated Time: 6 Hours
You diagnose:- A blocked-query pile-up and an idle-in-transaction leak — then size a pool against real resources so neither happens again
- Connection cost; pooling with an external pooler; pooling modes and their trade-offs; sizing pools against available resources
- Lock types and lock waits; deadlocks; blocked-query diagnosis
- Idle-in-transaction as a silent killer
Estimated Time: 7 Hours
You recover:- A database to a moment just before a destructive change — a graded point-in-time restore with a measured recovery objective, because an untested backup is not a backup
- Logical versus physical backup; continuous archiving; point-in-time recovery; backup tooling for large databases
- Retention and verification policy; tested restore drills with measured recovery objectives; corruption detection with checksums
- Recovering from the incidents that actually happen — a dropped table, a bad deployment, a lost node
Estimated Time: 7 Hours
You recover:- Service continuity through a node failure — a rehearsed failover under load, treated as a drill rather than a theory
- Streaming replication and replica configuration; synchronous versus asynchronous trade-offs; replication slots and their risks; replication lag and monitoring
- Logical replication for selective and cross-version replication; automated failover and cluster management; connection routing and read scaling
- Split-brain avoidance; rehearsed failover as a drill rather than a theory
Estimated Time: 6 Hours
You configure:- A hardened, access-controlled server — then plan and rehearse a major-version upgrade with a minimal-downtime approach and post-upgrade validation
- Authentication methods including certificate and modern token-based options; host-based access configuration; roles and privileges; row-level security
- Encryption in transit and at rest; auditing; secrets handling; patch strategy
- Major-version upgrade planning — in-place upgrade, logical replication cutover and minimal-downtime approaches; post-upgrade validation
Estimated Time: 6 Hours
You deliver:- One cluster taken from installation to tuned, replicated, monitored, secured and recoverable — and defended — after adding a vector-search workload and weighing managed cloud against self-hosted
- Monitoring and alerting with the standard observability stack; capacity planning; sharding and distributed extensions; read replicas
- Vector search workloads: index types, build cost, recall-versus-latency tuning and hybrid search alongside relational data; managed cloud services versus self-hosted — an honest account of what managed hides and what it doesn't
- Running databases on Kubernetes and when not to; migration from commercial databases; capstone: take one cluster to tuned, replicated, monitored, secured, recoverable — and defend it
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