Development & technology · SQL & Databases

AI training for SQL and Database Professionals

Databases are the memory of every application, and their performance, consistency and security determine whether entire systems succeed. AI coding assistants and agents support database developers and DBAs in writing complex queries, reading execution plans, designing schemas and index strategies and migrating between database platforms. They translate business questions into SQL, refactor sprawling stored procedures and generate test data. One principle still holds: a syntactically valid query is not necessarily a correct one, and an agent with write access to production data is a serious risk. In this course you learn to use AI productively with schema context, execution plans and tests, and to design access so that your data stays protected at all times.

Who is this course for?

  • Database and SQL developers
  • Database administrators for PostgreSQL, SQL Server, Oracle or MySQL
  • Backend developers responsible for data models and ORM layers
  • Data engineers and analytics engineers
  • BI developers working with complex reporting queries

Typical challenges we address

  • Slow queries and load spikes whose causes are hidden in execution plans
  • Sprawling stored procedures and triggers without documentation or tests
  • Risky schema changes and migrations on live systems
  • Different SQL dialects during platform changes or in multi-database estates

Practical scenarios from your field

Optimising SQL queries

Have execution plans from EXPLAIN ANALYZE or Query Store interpreted, identify bottlenecks such as full table scans or poor join orders and get rewritten queries and suitable indexes with a clear rationale.

Designing and reviewing schemas

Derive normalised data models with keys, constraints and data types from requirements, review existing schemas for redundancy and missing integrity rules and generate ER diagrams.

Refactoring stored procedures

Explain large PL/SQL or T-SQL procedures, convert cursor loops into set-based logic, standardise error handling and add tests with frameworks such as tSQLt or pgTAP.

Planning migrations safely

Generate versioned migration scripts for Flyway or Liquibase, design zero-downtime strategies such as expand and contract and assess rollback paths as well as effects on locking and replication.

Translating dialects

Migrate queries, functions and data types between Oracle, SQL Server, PostgreSQL and MySQL, account for differences in date functions, sequences and transaction behaviour and test for identical results.

Generating test data

Create realistic synthetic datasets that respect constraints, distributions and edge cases without using personal production data, for load tests and development environments. Generators live in the repository under version control.

Possible hands-on projects

Slow query analyser

A script that processes the slow query log or statistics views, groups the most expensive queries and reports concrete index and rewrite suggestions.

Schema documentation generator

A tool that reads a database schema and produces searchable documentation with table descriptions, relationships and an ER diagram. It refreshes automatically after each migration.

Text-to-SQL assistant

A small web app with read-only access to a reporting database that turns business questions into checked SQL and shows both query and result transparently.

All 10 courses for SQL and Database Professionals

Vibe & agentic coding (10)

Data protection & compliance

Databases often hold personal and business-critical data: you learn to give AI tools schemas and anonymised samples instead of production data, manage credentials securely and implement GDPR requirements such as data minimisation and deletion policies.

Prerequisites

You write SQL regularly, understand joins, aggregations and indexes and work with at least one relational database system.

Frequently asked questions

Should an AI agent access our database directly?

It can, but it needs careful planning. We recommend a dedicated read-only user, access to replicas or development databases rather than production, query timeouts and logging of every statement. Write changes go through versioned migration scripts with review. In the course you build exactly this safeguard, for instance through a restricted MCP server.

How reliable is AI-generated SQL?

It is usually syntactically correct, but semantic quality depends heavily on context: without the schema, relationships and business definitions, wrong joins or double counting creep in easily. With schema context, example queries and tests against known results, reliability rises considerably. You learn to validate generated queries systematically.

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Total €949 plus VAT