What if your AI coding agent actually understood your data stack? General AI tools can write SQL and catch obvious mistakes. But they cannot systematically detect anti-patterns, trace lineage, or keep warehouse costs under control. That gap can lead to inefficient queries, broken dependencies, and hidden compliance risks building up over time. I recently tried altimate-code, an open-source agent with 100+ tools purpose-built for data engineers, and built a demo repo to test it. From a single prompt, it created a DuckDB instance with my data, built a full dbt project with the medallion architecture, orchestrated pipelines with Airflow, and generated an interactive dashboard. What makes it different: • 100+ tools that analyze SQL through structural parsing, not text guessing • Works across your stack including Snowflake, BigQuery, Databricks, DuckDB, and more • Model-agnostic. Compatible with OpenAI, Anthropic, Gemini, Ollama, and others • Top scorer on ADE-Bench, outperforming Cursor, Claude Code, and Cortex on real data engineering tasks ☕️ Link to the demo repo: github.com/khuyentran1401/al… ️📦 Link to the repository: github.com/AltimateAI/altima… #DataEngineer #AI #LLM

DataPilot's Power User VSCode extension provides a seamless testing experience in dbt. With the extension, you can: 🔹 Validate your dbt code against best practices 🔹 Troubleshoot using real-time column lineage 🔹 Generate dbt tests with ease