SQL from Zero → Analyst
Complete path from SELECT basics to window functions. Preparation for data analyst role.
Structured paths from beginner to advanced — pick the track for your role, clear modules in order, stack Power.
Complete path from SELECT basics to window functions. Preparation for data analyst role.
SQL for server development: CRUD, query optimization, transactions.
Practical SQL for manual QA: DB checks, fixture seeding and cleanup, cross-table validations and typical QA scenarios.
Schemas and DDL, window functions, CTEs and complex subqueries — what data marts and denormalised layers are built on.
Tasks from real technical interviews: Google, Microsoft, Amazon, LinkedIn, Yandex, Sberbank and more.
Practical formats: debugging, data quality, indexes, EXPLAIN, transactions, migrations, schema design.
Tasks written for ClickHouse itself: aggregates over large tables, arrays, window functions. The dialect differs from PostgreSQL not in syntax but in what is cheap — and the tasks show it.
The same path from SELECTs to subqueries, but every task runs on MySQL — the sandbox uses its engine, and the differences from PostgreSQL show up where you would expect them.
The BigQuery dialect: arrays and structs instead of plain rows, partitioning that decides what a query costs, and window functions on top.
From the first OVER to frames, moving averages and ranking within groups. The topic interviews ask about most and courses teach last.
Read the plan, add the index, rewrite the query so it stops scanning everything. Here the result matters less than the query that produced it.
Tasks where the answer does not fit into one SELECT: intermediate steps, branching, recursion. What real reports and data marts are assembled from.