SQL and Data Handling for AI: Advanced

Level 0, topic 4 (part 2 of 2) of the BitByte AI path, for learners who finished SQL and Data Handling for AI: Beginner. It covers what AI and data engineers do with databases every day: analytics SQL, sound schemas, safe concurrent changes, fast queries, secure access, moving and cleaning data with Python, reliable pipelines, training datasets that do not leak the future, and SQL inside LLM and RAG applications.

You get a bilingual written tutorial where every example was run on SQLite, MySQL or PostgreSQL and its output checked, hand-picked videos from IBM Technology, ByteByteGo, Hussein Nasser, Decomplexify, techTFQ and others, and three exams.

• Write analytics queries with CTEs, recursive CTEs and window functions: rankings, running totals, growth, cohorts and gaps-and-islands
• Design normalised schemas from requirements, and change them safely with migrations
• Use transactions, isolation levels and locking to keep data correct under concurrency
• Speed up queries with the right indexes and by reading EXPLAIN plans
• Secure databases with roles and least privilege, and prevent SQL injection with parameters
• Move data between SQL, Python, pandas, CSV, JSON, Excel and Parquet; clean messy data; build idempotent ETL pipelines
• Build leakage-free, reproducible ML datasets with SQL and pandas, and use SQL in AI apps, including vector search with pgvector

• The beginner course: SQL and Data Handling for AI: Beginner
• Python with pandas (the "Python for AI" course)
• Optional: Docker, to run MySQL and PostgreSQL locally for the server examples