PostgreSQL Database Training

RexGalaxy Academy's PostgreSQL Database Training is a practical database development and support program designed for learners who want to build strong SQL, schema design, reporting, analytics, backend database, MIS and database support skills using PostgreSQL.

This course covers the complete PostgreSQL learning path from database foundations to portfolio-ready project work. Students learn PostgreSQL setup, psql, pgAdmin, database objects, SQL query writing, tables, data types, constraints, relationships, joins, aggregation, subqueries, CTEs, window functions, views, indexes, EXPLAIN plans, transactions, locks, PL/pgSQL functions, procedures, triggers, roles, privileges, backup, restore, JSONB, extensions, analytics and AI-assisted SQL practice.

The training is built around guided labs and project work. Learners practice creating databases, designing schemas, writing clean SELECT queries, building reports, modelling relationships, creating constraints, using indexes, reading query plans, handling transactions, writing stored logic, managing users and privileges, importing CSV data, preparing backups, using JSONB, reviewing AI-generated SQL safely and documenting a complete PostgreSQL capstone project.

Students should be able to:

• Build PostgreSQL databases from fundamentals to portfolio-ready demonstrations.

• Use SQL, joins, CTEs, views, indexes, PL/pgSQL, roles, backup and restore workflows.

• Design clean schemas with keys, constraints, relationships, sample data and data dictionaries.

• Practice reporting, analytics, query tuning, documentation and interview-ready explanations.

• Create portfolio artifacts including SQL workbooks, ERD notes, schema packs, tuning reports, admin checklists, analytics reports, backup evidence and a final PostgreSQL capstone.

• Prepare for SQL developer, database support, backend database, MIS, analytics, reporting and junior database administration roles.

Build a clear foundation in database concepts, PostgreSQL tools, server workflow and safe practice habits.

• Understand database purpose for structured data, tables, rows, columns, business records, applications, reporting and decision support.

• Learn DBMS concepts such as relational model, schema, database engine, client-server architecture, metadata and consistency rules.

• Explore PostgreSQL ecosystem including PostgreSQL Server, psql, pgAdmin, extensions, documentation, community support and release awareness.

• Prepare a lab environment with local installation, sample databases, connection settings, service status, ports and safe practice workspace.

• Use client tools including psql basics, pgAdmin navigation, query tool usage, object browser, script files and result export workflow.

• Review database objects such as databases, schemas, tables, views, sequences, functions, indexes, constraints and roles overview.

• Understand SQL categories including DDL, DML, DCL, TCL, SELECT workflow and how commands support real database operations.

• Follow naming and organization habits such as clean names, lowercase convention awareness, comments, folders, file versions and script discipline.

• Build documentation habits with ERD notes, table dictionary, setup steps, query notes, backups, screenshots and change history.

• Lab outcome: create database, connect with psql and pgAdmin, create first table, insert sample data and run verification queries.

Write clean SELECT queries with filtering, sorting, aliases, expressions, limits and result interpretation.

• Choose columns, read result sets, use aliases, calculate expressions and format query output clearly.

• Filter data with WHERE clause, comparison operators, logical operators, BETWEEN, IN, LIKE, ILIKE and NULL checks.

• Sort and limit results using ORDER BY, ascending and descending order, LIMIT, OFFSET, pagination awareness and top records.

• Use DISTINCT for duplicate removal, unique category lists, data exploration and business lookup preparation.

• Apply string functions such as concatenation, length, trim, upper, lower, substring awareness and readable text transformations.

• Work with date basics including current date, timestamps, intervals, date filtering, simple date calculations and reporting periods.

• Use numeric expressions for arithmetic, rounding, type casting awareness, calculated columns and business metric fields.

• Handle NULL behavior with IS NULL, COALESCE, missing data meaning, default display values and safe condition writing.

• Build query readability with indentation, aliases, comments, clause order, meaningful names and reusable SQL script habits.

• Lab outcome: create employee list, product search, sales filters, date range report, customer status report and clean query practice.

Design tables correctly with PostgreSQL data types, keys, constraints, defaults and validation rules.

• Learn table design basics including entities, attributes, rows, columns, business meaning, naming conventions and schema planning.

• Use PostgreSQL data types such as integer, bigint, numeric, varchar, text, date, timestamp, boolean, UUID, JSONB and arrays awareness.

• Design primary keys with unique row identity, generated identity columns, UUID awareness, natural versus surrogate keys and stable references.

• Create foreign keys for parent-child relationships, referential integrity, update and delete actions and dependency awareness.

• Apply constraints such as NOT NULL, UNIQUE, CHECK, DEFAULT, primary key and foreign key for reliable data quality rules.

• Use sequences and identity columns for generated values, sequence purpose, reset awareness and safe business record usage.

• Practice DDL operations including CREATE, ALTER and DROP awareness, renaming columns, adding constraints and safely modifying structures.

• Organize schemas using public schema, custom schemas, object grouping, permission awareness and cleaner database organization.

• Plan data quality checks for duplicate prevention, invalid values, missing values, consistent codes and validation rules.

• Lab outcome: create student, product, order and payment tables with keys, constraints, sample data and validation checks.

Work with relationships, joins, ERD design, normalization and multi-table business queries.

• Understand relationship concepts including one-to-one, one-to-many, many-to-many, parent-child records, lookup tables and junction tables.

• Write INNER JOIN queries for matching records, table aliases, join conditions, relationship checks and readable report queries.

• Use LEFT JOIN for optional data, unmatched records, NULL interpretation, missing relationships and investigation use cases.

• Review RIGHT and FULL JOIN awareness for complete comparison, unmatched rows from both sides and when simpler alternatives work.

• Practice self joins for hierarchy data, manager-employee examples, category trees and alias-based table reuse.

• Understand join filtering with WHERE versus ON, filter placement, accidental row loss prevention and row count validation.

• Apply ERD thinking with entities, attributes, keys, relationships, cardinality, optionality and business process mapping.

• Learn normalization basics including reducing duplication, first to third normal form awareness, lookup design and practical balance.

• Document data models with table purpose, key fields, relationship notes, sample rows and maintenance clarity.

• Lab outcome: create customer-order report, product-sales report, employee-manager query, many-to-many mapping and ERD practice.

Create reporting queries with aggregate functions, grouping, HAVING filters, subqueries, CTEs and set logic.

• Use aggregate functions such as COUNT, SUM, AVG, MIN, MAX and DISTINCT awareness for business metric calculation.

• Group data by category, city, department, date, product and customer for summary report creation.

• Apply HAVING filters for grouped results, comparing totals, minimum counts, revenue thresholds and group conditions.

• Use date grouping with daily, monthly and yearly summaries, date_trunc awareness, period reporting and trend analysis basics.

• Apply conditional aggregation with CASE expressions, category totals, active counts and simple dashboard metrics.

• Use subqueries including scalar subquery, IN subquery, EXISTS awareness, nested query logic and use case selection.

• Write CTEs using WITH clause for readable step-by-step queries, reusable intermediate results and report clarity.

• Use set operations such as UNION, UNION ALL, INTERSECT and EXCEPT awareness with duplicate handling and same-column rules.

• Validate reports by comparing totals, checking row counts, testing filters, identifying outliers and avoiding wrong conclusions.

• Lab outcome: build sales summary, city-wise revenue, top customer, unpaid order, department analysis, CTE and set operation tasks.

Use advanced PostgreSQL SQL features for analytics, transformations, reusable logic and cleaner reports.

• Use window functions such as ROW_NUMBER, RANK, DENSE_RANK, running totals, partitioning, ordering and analytics use cases.

• Apply CASE expressions for conditional values, business buckets, status labels, score bands and flexible calculated fields.

• Use date and time functions including date_trunc, extract, intervals, age awareness, timestamp handling and period-based reporting.

• Process strings with split_part, concat, position awareness, regexp matching basics, cleaning text and output formatting.

• Practice type casting with explicit casts, safe conversions, numeric precision, date conversion and avoiding invalid data errors.

• Understand array awareness including array columns, simple array operators, unnest awareness and when normalized tables are better.

• Use Common Table Expressions for multi-step reporting, recursive CTE awareness, hierarchical data and readability habits.

• Apply PostgreSQL operators for pattern matching, comparison logic, NULL-safe thinking and useful SQL expression patterns.

• Build reusable query designs with consistent aliases, comments, test cases, saved scripts and business explanation notes.

• Lab outcome: create ranking report, running total, customer segmentation, monthly trend, text cleaning, date summary and CTE practice.

Use views, indexes, EXPLAIN plans and tuning habits to make PostgreSQL work reusable and efficient.

• Create views for reusable SELECT logic, report simplification, controlled access awareness, dependency checks and maintenance rules.

• Understand materialized views for stored result sets, refresh concepts, reporting performance and freshness trade-offs.

• Learn index basics including B-tree indexes, unique indexes, composite indexes, expression index awareness and write overhead.

• Select indexes based on filter columns, join columns, sort columns, composite order, high-cardinality columns and avoiding useless indexes.

• Review special indexes such as GIN for JSONB and full-text search, GiST awareness and matching index type to query need.

• Read EXPLAIN basics including sequential scan, index scan, estimated rows, cost awareness and plan-reading habits.

• Understand EXPLAIN ANALYZE awareness for actual execution insight, timing, row count differences and careful production use.

• Improve performance habits by selecting needed columns, filtering early, avoiding unnecessary functions, reducing wildcard misuse and checking joins.

• Review maintenance awareness including VACUUM, ANALYZE, table bloat awareness, statistics and practical database health checks.

• Lab outcome: create views, add indexes, compare EXPLAIN output, tune slow reports and document performance improvements.

Protect data consistency using ACID concepts, MVCC, transactions, rollback, isolation and locking basics.

• Understand ACID concepts including atomicity, consistency, isolation, durability, business reliability and why transactions protect data.

• Practice transaction workflow with BEGIN, COMMIT, ROLLBACK, savepoints awareness, autocommit behavior and safe testing practices.

• Explore use cases such as payments, stock updates, order placement, booking systems, account transfer and multi-step business actions.

• Learn MVCC awareness including read consistency, concurrent sessions, old row versions, snapshots and why reads and writes can overlap.

• Review isolation awareness including read committed, repeatable read, serializable awareness and practical consistency decisions.

• Understand locking basics including row locks, table locks awareness, blocking, lock wait symptoms and transaction duration problems.

• Learn deadlock awareness including competing updates, detection, retry thinking and writing safer transaction sequences.

• Practice data modification safety with SELECT before UPDATE, affected row checks, transaction scripts and rollback before commit habit.

• Connect integrity design with constraints, foreign keys, transactions and application validation working together.

• Lab outcome: account transfer, stock deduction, order creation rollback, savepoint demo, lock demonstration and integrity checklist.

Create reusable PostgreSQL database logic with functions, procedures, triggers, variables and controlled automation.

• Understand PL/pgSQL purpose for reusable logic, business rules close to data, controlled execution and database automation.

• Create functions with parameters, return values, language selection, volatility awareness and testing workflow.

• Use variables and control flow including DECLARE, BEGIN, IF, CASE, loops, assignment and readable procedural structure.

• Review procedures with CALL workflow, transaction-related thinking, use cases and difference from functions.

• Handle exceptions with predictable errors, validation messages, safe failure handling and clear debugging information.

• Understand cursor awareness for row-by-row processing, when to avoid it, set-based alternatives and performance caution.

• Create triggers including BEFORE and AFTER triggers, row-level logic, audit logging, validation support and hidden behavior caution.

• Design audit logic with change history tables, timestamps, users, old and new values awareness and compliance-friendly records.

• Follow reusable code habits such as naming, comments, input validation, testing scripts, version control and deployment notes.

• Lab outcome: create validation function, reporting function, audit trigger, reusable procedure and stored logic review checklist.

Manage PostgreSQL users, permissions, schemas, backups, restore workflow, logs and operational safety.

• Learn administration basics including clusters, databases, schemas, connections, sessions, configuration files awareness and service control.

• Manage roles and users including login roles, group roles, password rules, role membership, ownership and least privilege thinking.

• Control privileges with GRANT, REVOKE, schema access, table access, sequence access, function access and permission troubleshooting.

• Build security habits with strong credentials, limited superuser use, safe public schema behavior, SSL awareness and access review.

• Practice backup basics such as pg_dump, custom format, plain SQL dump, backup naming, schedules, retention and verification habit.

• Understand restore workflow including psql restore, pg_restore awareness, target database, role issues, extension dependencies and test restore.

• Import and export using COPY awareness, CSV loading, delimiter issues, encoding, headers, data validation and error handling.

• Monitor PostgreSQL with connection count, active queries, logs, slow queries, disk space, table size and health checks.

• Review maintenance concepts including VACUUM, ANALYZE, autovacuum awareness, statistics, bloat awareness and production safety.

• Lab outcome: create users, assign privileges, perform dump and restore, import CSV, review logs and create admin checklist.

Use modern PostgreSQL capabilities for semi-structured data, extensions, analytics, search and AI-assisted SQL practice.

• Understand JSONB basics for semi-structured data, key-value access, nested documents, validation awareness and modelling trade-offs.

• Query JSONB by extracting values, filtering JSON data, update awareness, GIN index awareness and report preparation.

• Review full-text search awareness with tsvector, tsquery concept, search ranking awareness and practical search use cases.

• Explore extensions such as pgcrypto, uuid-ossp awareness, PostGIS awareness, extension installation concept and controlled feature use.

• Build analytics queries for cohorts, top-N reports, retention ideas, funnel-style thinking and dashboard-ready output.

• Practice data cleaning for duplicates, inconsistent formats, missing values, invalid categories, type conversion and quality flags.

• Use AI-assisted SQL safely for prompt writing, query ideas, explaining query logic, generating test data and checking SQL with human validation.

• Follow AI safety habits: never paste private credentials, verify generated SQL, test on sample data and review before production use.

• Prepare business reporting with KPI definitions, data dictionary, validation totals, export formats and stakeholder explanation notes.

• Lab outcome: JSONB customer profile, extension demo awareness, analytics report, AI query review and data quality checklist.

Complete a PostgreSQL project and prepare interview-ready explanations, documentation and career direction.

• Plan capstone around business requirements, tables, relationships, keys, sample data, reports, security, backup and milestones.

• Choose project options such as inventory system, sales analytics, student records, CRM, finance tracker, HR database or ecommerce reporting.

• Prepare database design evidence including ER diagram, schema notes, table dictionary, constraints, sample data and relationship explanation.

• Build query evidence with CRUD scripts, joins, aggregation, CTEs, views, window functions awareness and verified result screenshots.

• Prepare performance evidence including indexes, EXPLAIN screenshots, before-after notes, query improvement explanation and tuning decisions.

• Add administration evidence including role setup, privileges, backup file, restore notes, import-export steps and operations checklist.

• Document the project with README, setup guide, SQL scripts, screenshots, data model, known limits and troubleshooting notes.

• Revise interview topics such as SQL, joins, keys, constraints, indexes, transactions, views, functions, backup, security and normalization.

• Practice scenarios including slow query, duplicate data, failed import, missing permission, wrong join, rollback need and backup restore.

• Final outcome: deliver a portfolio-ready PostgreSQL project and confidently explain database design, SQL, admin and reporting.