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Advanced SQL for Analytics in 21 days

Execution plans, partitioning, materialized views, PostgreSQL advanced features, and data modeling.

  • Daily plan, 45-60 min a day
  • 6 lessons + 18 exercises
  • AI tutor included
duration21-day sprint
Topics6 total
Exercises18 total
levelAdvanced
  • No prior coding experience required
  • Practice tasks every day
  • Build impressive portfolio project

What you will learn

A powered by mentors Advanced plan with structured subtopics, quizzes, and practice tasks.

01Challenge

Query execution plans in depth

Interpret EXPLAIN ANALYZE, understand node types, and eliminate bottlenecks.

02Challenge

Table partitioning

Speed up large table queries with range, list, and hash partitioning.

03Challenge

Materialized views and refresh strategies

Pre-compute expensive queries and choose between concurrent and full refresh.

04Challenge

PostgreSQL advanced features

Use JSONB, arrays, full-text search, and stored procedures in PostgreSQL.

05Challenge

Data modeling patterns

Compare star schema, snowflake, and data vault for different workloads.

06Challenge

Analytical databases and columnar storage

Understand how DuckDB, BigQuery, and Redshift optimize for analytical queries.

See the quiz + practice flow

Three answered questions and a filled code task so you know exactly what to expect.

Quiz preview

2/3 correct

1. How long is the SQL for Analytics plan?

Correct
21-day sprint
5-day sprint
7-day sprint
10-day sprint

2. What level is this SQL for Analytics plan?

Incorrect
Beginner
Intermediate
Advanced
Expert

3. Which of these appears in the SQL for Analytics outline?

Correct
Query execution plans in depth
SQL for Analytics workflow playbook
SQL for Analytics best-practice checklist
SQL for Analytics case study

Code practice preview

Submitted

Format a lesson title

Build a helper that formats a lesson label with a padded index and title.

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