SQL Course

Welcome to the SQL Engineering track on TechLearningPRO — a complete, production-grade roadmap from your very first SELECT to designing multi-tenant, billion-row, ACID-compliant…

A database is an organized collection of data that a program can read, write and search efficiently.

SQL — Structured Query Language — is a declarative language for talking to relational databases.

Both store data, but they make different trade-offs.

A DBMS is the software that runs your database — it accepts SQL, manages files on disk, handles concurrent users, enforces constraints, replicates data and recovers from crashes.

To learn SQL the right way you need a real database on your machine.

A database is a logical container for tables.

Tables are the heart of relational databases.

Choosing the right data type for each column controls correctness, storage cost and performance.

INSERT adds new rows to a table.

SELECT reads data.

WHERE filters rows before grouping or projection.

ORDER BY sorts the result.

LIMIT caps the number of returned rows.

Aliases rename columns or tables in a query for readability.

Database design is the practice of modelling real-world entities (users, orders, products) into tables, columns and relationships.

A primary key uniquely identifies each row in a table.

A foreign key is a column that references the primary key of another table — the way relational databases enforce relationships.

Relational databases get their name from relationships between tables.

A 1:1 relationship means each row in table A maps to exactly one row in table B.

1:N is the most common relationship — one parent row has many children.

M:N means each row in A can relate to many in B and vice-versa.

Normalization is the process of organizing data to remove redundancy and dependency anomalies.

Denormalization deliberately introduces redundancy to make reads cheaper.

Entity-Relationship (ER) diagrams visually show entities, attributes and relationships before any SQL is written.

Aggregate functions collapse many rows into one summary value: COUNT, SUM, AVG, MIN, MAX.

GROUP BY partitions rows into groups so aggregates apply per group.

HAVING filters groups, not rows.

A subquery is a SELECT inside another SQL statement.

Beyond simple subqueries, nested queries can be many levels deep — answering complex business questions in one statement.

CASE is SQL's if/else.

Window functions compute a value across a set of rows related to the current row, without collapsing them.

A CTE (WITH … AS (…)) is a named, in-query temporary result.

A view is a stored named query — it behaves like a virtual table.

A stored procedure is a named block of SQL/PLpgSQL/T-SQL stored in the DB.

INNER JOIN returns rows where the join condition matches in both tables.

LEFT JOIN returns all rows from the left table, plus matching rows from the right (NULL where none match).

RIGHT JOIN is the mirror of LEFT JOIN — it keeps all rows from the right table.

FULL OUTER JOIN keeps unmatched rows from both sides.

A self join joins a table to itself.

CROSS JOIN returns the Cartesian product of two tables — every combination of A × B.

Real apps regularly join 4–8 tables in one query — orders, users, products, line items, addresses, payments.

Joins are where queries live or die.

Relationship queries answer 'who is connected to whom' — friend-of-friend, customers who bought X also bought Y, manager → reports.

Real production join queries combine all you've learned: filters, multiple joins, aggregates, CTEs and indexes.

Query optimization turns slow queries into fast ones.

An index is a data structure (usually a B-tree) that lets the DB jump to rows by column value in O(log n) instead of O(n).

A composite index covers multiple columns.

An execution plan is the optimizer's chosen recipe to run your query.

Performance work is methodical: find slow queries, understand their plan, fix with indexes/rewrites, verify with measurements.

Caching avoids hitting the DB.

Partitioning splits a giant table into smaller physical pieces — by range (date), list (region) or hash (user_id).

When one DB box isn't enough, scale via vertical (bigger box), read replicas (copies for reads), sharding (split by key), and caching.

A consolidated checklist used by senior engineers to keep databases fast — at small and large scale.

Tuning live queries safely takes process: capture, reproduce on a copy, fix, ship behind a feature flag, measure.

A transaction is a group of SQL statements that either all succeed or all fail — atomically.

ACID — Atomicity, Consistency, Isolation, Durability — is the contract a relational DB makes.

COMMIT finalizes a transaction; ROLLBACK undoes it.

Transactions running concurrently can see each other's partial work — unless prevented by an isolation level.

Locks serialize concurrent access to rows/tables to keep data consistent.

SQL Injection happens when user input is concatenated into SQL strings — letting attackers run their own queries.

Beyond SQL injection: least privilege, encryption, audit, network isolation, secrets management.

Databases support fine-grained roles, users, GRANTs and REVOKEs.

Backups save your business when something goes wrong — accidental DROP, ransomware, hardware failure.

A consolidated security checklist for production DBs — every box must be checked before any sensitive workload goes live.

An e-commerce DB is the canonical project to learn relational design — users, products, carts, orders, line items, payments, reviews.

Banking schemas demand strict ACID, double-entry ledgers, immutable history and audit logs.

Dashboards need fast aggregates over big tables — DAU/MAU, revenue, retention, funnels.

Auth schemas store users, passwords (hashed!), sessions, tokens, MFA.

Inventory schemas track stock, movements, warehouses with strong consistency to avoid overselling.

SaaS apps store many tenants' data in one DB.

Reporting workloads are read-heavy, scan-large, and run by humans on demand.

A data warehouse is a separate analytics-optimized DB — star schema, columnar storage, MPP.

Production schemas evolve with the business.

At enterprise scale, SQL lives inside a wider data platform: OLTP DBs, replicas, ETL, warehouse, lakehouse, BI, governance, lineage.

Theory only sticks once you've solved real problems.

Harder query challenges that test creative SQL — recursive CTEs, gaps & islands, sessionization, percentiles.

Design rounds test your ability to model real systems.

Practical tuning tasks — given a slow query, make it fast.

Bug-hunting practice: queries that look right but return wrong data.

A curated, reusable bank of SQL interview questions covering fundamentals to advanced — exactly what FAANG, fintech and SaaS hiring managers ask.

Project-style exercises that mirror real work: build the schema, write the queries, optimize, and present trade-offs.

Lessons from real production incidents and migrations: GitLab's 2017 backup loss, Stripe's MongoDB→Postgres trade-offs, Shopify's MySQL sharding (Vitess).

Open-ended scenarios that test full engineering judgement: scale a slow service, recover from corruption, migrate without downtime, design for compliance.

Capstone scenarios combining everything — design + scale + security + ops.