SQL in Data & Analytics

Learn and apply 25 Snowflake Semantic View modeling patterns with interactive tutorials and examples.

Master advanced SQL features, stored procedures, and warehouse optimization on Databricks.

Add AI tasks like classification, extraction, and summarization directly to SQL and PySpark pipelines.

Diagnose slow SQL queries and fix them with the right indexes and rewrites.

Enable C-suite agents to query each other, coordinate analysis, and prevent communication loops.

Generate SQL queries from plain English descriptions for BigQuery, PostgreSQL, MySQL, and more.

Design schemas, optimize queries, and choose the right database for your application.

Recover context from interrupted Claude sessions and continue work without replaying full history.

Import live database schemas and create semantic layers from snapshot files.

Query your KTX database to analyze data, find records, and explore metrics.

Map dbt models and sources into semantic layer overlays with column metadata.

Identify recurring SQL query patterns across database tables for analysis.

Convert SQL table usage data into typed evidence for schema projection.

Write declarative data visualization queries combining SQL with grammar of graphics principles.

Query US congressional stock trades and politician investment activity with rich filters.

Optimize ClickHouse queries and design high-performance analytical databases.

Build and manage semantic layers that map databases to business metrics and dimensions.

Query wallet balances, token portfolios, transactions, and NFTs on Solana with USD pricing.

Build and optimize Snowflake data pipelines, SQL queries, and AI-powered applications.

Write optimized Snowflake queries using QUALIFY, FLATTEN, and semi-structured data patterns.