Written by Alex Merced Developer from devNursery.com and alexmercedcoder.dev You should follow him on Twitter and checkout his articles on LogRocket.
How goal-directed analytics agents decompose business questions into sub-tasks, execute action loops over Apache Iceberg tables, and use the lakehouse as both a data source and a state store for agent action logs.
Iceberg v3 row lineage adds _row_id and _last_updated_sequence_number to every table, enabling native change data capture without Debezium or Kafka. Technical deep dive with Snowflake and Databricks examples.
Apache Iceberg v3 deletion vectors replace positional delete files with binary bitmaps in Puffin files, delivering up to 10x faster DML on Snowflake. Deep dive into architecture, benchmarks, and migration.
Iceberg views standardize SQL view definitions across engines, enabling view federation across Polaris, Nessie, and Gravitino catalogs. How Snowflake Horizon, Databricks Unity Catalog, and open source catalogs handle portable SQL.
Microsoft Build 2026 revealed an agentic analytics stack built on Fabric IQ, OneLake Iceberg support, and semantic models. The architecture shows how Microsoft competes with open lakehouse platforms.
How to build a Model Context Protocol (MCP) server that exposes lakehouse tables and semantic views as AI-accessible tools, with Python implementation patterns and authentication.
