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Database Master: Become an SQL Pro in 30 Days

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Database Master: Become an SQL Pro in 30 Days—

Becoming an SQL pro in 30 days is an ambitious but achievable goal if you follow a focused, practical plan. This guide gives you a day-by-day roadmap, learning priorities, exercises, and project ideas to move from beginner to confident practitioner. It emphasizes hands-on practice, real-world scenarios, and measurable milestones so you build lasting skills rather than memorizing syntax.

Why 30 days?

Thirty days provides a compact timeframe that encourages consistent daily practice without burning out. Rather than trying to learn everything, this plan concentrates on the most useful SQL concepts and techniques used by developers, analysts, and database administrators: data modeling, querying, indexing, performance tuning, transactions, and basic administration. By the end you’ll be able to design schemas, write complex queries, optimize performance, and interact with a relational database professionally.

How to use this plan

  • Spend 60–90 minutes per day (up to 2–3 hours on weekends).
  • Use a local database (PostgreSQL or MySQL recommended) or an online SQL playground (db-fiddle, SQLBolt, Mode).
  • Keep a notebook of queries, schema designs, and performance experiments.
  • Build one capstone project (see Day 25–30) and iterate on it.
  • When stuck, read official docs and ask targeted questions.

Tools & setup (Day 0)

  • Install PostgreSQL (recommended) or MySQL.
  • Install a GUI client: DBeaver, pgAdmin, TablePlus, or DataGrip.
  • Optional: Install Docker to run database containers.
  • Get a dataset: Kaggle, public CSVs, or generate synthetic data with scripts.

Week 1 — Foundations (Days 1–7)

Day 1 — SQL basics

  • SELECT, FROM, WHERE, LIMIT.
  • Filtering with =, <>, <, >, BETWEEN, IN, LIKE.
  • Exercise: Explore a sample “employees” table; write simple selects.

Day 2 — Aggregations

  • COUNT, SUM, AVG, MIN, MAX, GROUP BY, HAVING.
  • Exercise: Compute total sales, average order value, top customers.

Day 3 — Joins

  • INNER JOIN, LEFT/RIGHT JOIN, FULL OUTER JOIN, CROSS JOIN.
  • Understand join conditions vs Cartesian products.
  • Exercise: Combine customers, orders, and products tables.

Day 4 — Subqueries & CTEs

  • Inline subqueries, correlated subqueries.
  • WITH (CTE) for readability and recursive queries.
  • Exercise: Find customers with orders above their average order size.

Day 5 — Window functions

  • ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE(), LEAD/LAG, SUM() OVER().
  • Use cases: running totals, top-N per group.
  • Exercise: Top 3 products per category by sales.

Day 6 — Data definition

  • CREATE TABLE, ALTER TABLE, DROP TABLE.
  • Data types: integer, bigint, numeric, text, varchar, date, timestamp, boolean, JSON/JSONB.
  • Constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK.
  • Exercise: Design a normalized schema for a simple e-commerce site.

Day 7 — Data modification & transactions

  • INSERT, UPDATE, DELETE, TRUNCATE.
  • BEGIN, COMMIT, ROLLBACK; ACID basics, isolation levels overview.
  • Exercise: Run batched inserts and experiment with rollback.

Week 2 — Practical skills & modeling (Days 8–14)

Day 8 — Normalization & schema design

  • 1NF, 2NF, 3NF; when to denormalize.
  • Entity-relationship modeling, primary/foreign keys.
  • Exercise: Convert a denormalized dataset into a normalized schema.

Day 9 — Indexing fundamentals

  • B-tree, hash, GIN/GiST (Postgres).
  • How indexes speed up reads and cost writes.
  • When to add composite indexes and covering indexes.
  • Exercise: Add indexes and measure query speedups.

Day 10 — Advanced joins & set operations

  • SELF JOIN, anti-joins (NOT EXISTS, LEFT JOIN … IS NULL).
  • UNION, UNION ALL, INTERSECT, EXCEPT.
  • Exercise: Deduplicate records and find unmatched entries.

Day 11 — Working with text & dates

  • String functions: CONCAT, SUBSTRING, TRIM, POSITION, REGEXP.
  • Date functions: DATE_TRUNC, AGE, INTERVAL arithmetic.
  • Exercise: Parse and normalize messy phone numbers or timestamps.

Day 12 — JSON/NoSQL in SQL

  • Storing JSON (JSONB in Postgres), querying with -> and ->> operators.
  • Indexing JSON fields (GIN).
  • Exercise: Migrate a semi-structured log dataset into JSONB and query it.

Day 13 — Views, materialized views & stored procedures

  • CREATE VIEW, RULES, MATERIALIZED VIEW and refresh strategies.
  • Functions and stored procedures basics (PL/pgSQL example).
  • Exercise: Create a materialized view for expensive aggregations.

Day 14 — Security & access control

  • GRANT, REVOKE, roles, least privilege.
  • Connection encryption basics and safe credential storage.
  • Exercise: Create roles for read-only analysts and app users.

Week 3 — Performance & scaling (Days 15–21)

Day 15 — EXPLAIN and query planning

  • EXPLAIN, EXPLAIN ANALYZE.
  • Reading an execution plan: seq scan, index scan, join algorithms.
  • Exercise: Diagnose a slow query and propose index changes.

Day 16 — Advanced indexing strategies

  • Partial indexes, expression indexes, covering indexes, index-only scans.
  • When indexes hurt performance.
  • Exercise: Create partial indexes for filtered queries.

Day 17 — Partitioning & sharding basics

  • Range, list, hash partitioning (Postgres syntax).
  • Logical vs physical sharding; application-level routing.
  • Exercise: Partition a large table by date and query efficiently.

Day 18 — Connection pooling & concurrency

  • PgBouncer, connection limits, pooling modes.
  • Locking basics: row-level locks, deadlocks, lock escalation.
  • Exercise: Simulate concurrent updates and resolve deadlocks.

Day 19 — Caching & read-replicas

  • Query caching patterns, materialized views, Redis caching.
  • Read replicas for scaling reads; lag considerations.
  • Exercise: Design a read-heavy architecture for analytics.

Day 20 — Backup & restore strategies

  • Logical (pg_dump) vs physical backups, point-in-time recovery (PITR).
  • Automating backups and validating restores.
  • Exercise: Take a backup and restore to a new instance.

Day 21 — Monitoring & observability

  • Key metrics: QPS, latency, locks, cache hit ratio.
  • Tools: pg_stat_statements, Prometheus + Grafana.
  • Exercise: Set up basic monitoring dashboard for query latency.

Week 4 — Advanced topics & capstone (Days 22–30)

Day 22 — Data warehousing basics

  • OLTP vs OLAP, star schema, dimensions and facts.
  • ETL vs ELT, batch vs streaming.
  • Exercise: Design a star schema for sales analytics.

Day 23 — Analytics SQL & windowing at scale

  • Complex windowing, rolling aggregates, percentiles.
  • Approximate algorithms (HyperLogLog, t-digest).
  • Exercise: Build percentiles and running aggregates for user metrics.

Day 24 — Migration & schema evolution

  • Zero-downtime migrations, blue-green deploys, online schema changes (pg_repack, gh-ost).
  • Handling backfills and data migrations safely.
  • Exercise: Perform a safe column rename and backfill.

Day 25 — Security hardening & compliance

  • Encryption at rest/in transit, auditing, data masking.
  • GDPR/CCPA basics for DB design (right to be forgotten, export).
  • Exercise: Implement column-level encryption for PII.

Day 26 — Stored procedures, triggers & advanced PL

  • Use cases and pitfalls for triggers.
  • Writing robust stored procedures and error handling.
  • Exercise: Create an audit trigger that logs data changes safely.

Day 27 — Real-time and streaming integrations

  • Change Data Capture (CDC) with Debezium, Kafka basics.
  • Streaming queries and materialized views refresh patterns.
  • Exercise: Set up a simple CDC pipeline to stream table changes.

Day 28 — Testing and CI for databases

  • Unit testing migrations (pgTAP), schema linting, migration rollbacks.
  • Automating DB tasks in CI/CD pipelines.
  • Exercise: Add DB testing to a sample repo.

Day 29 — Soft skills & collaboration

  • Communicating DB design to engineers and non-DBAs.
  • Code review for queries and schema changes.
  • Exercise: Create documentation for your schema and run a mock review.

Day 30 — Capstone project

  • Build a complete small app or analytics pipeline using what you learned. Examples:
    • E-commerce backend: schema, indexes, order queries, analytics dashboard.
    • Event analytics: ingest events, store in partitions/JSONB, build aggregated reports.
    • Library system: borrowing history, fines, recommendations using window functions.
  • Deliverables: schema SQL, sample data, key optimized queries, README with decisions and monitoring plan.

Example 30-day study schedule (compact)

  • Weekdays: 60–90 minutes (read + exercises).
  • Weekends: 2–3 hours (bigger hands-on tasks and project work).
  • Keep a Git repo with schema, sample data, queries, and notes.

Tips for faster progress

  • Focus on concepts, not memorizing syntax; use docs as your “cheat sheet.”
  • Always measure with EXPLAIN ANALYZE before and after optimizations.
  • Practice reading real-world schemas and query logs.
  • Use pair-programming or community help to get feedback on designs.
  • Build, break, and fix — the fastest learning happens when you debug real issues.

Resources & further reading

  • PostgreSQL official documentation — comprehensive, authoritative.
  • High Performance MySQL / PostgreSQL books for deep dives.
  • Online interactive SQL courses (Mode SQL, SQLBolt, LeetCode SQL).
  • Blogs and talks on query tuning, indexing, and database internals.

Becoming a “Database Master” is a continuous journey. This 30-day plan gives you a strong, practical foundation; keep building with projects, reading source docs, and diagnosing real performance problems.

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