A hands‑on walkthrough showing how to:
• Auto‑ingest JSON drops from S3 into a landing table with Snowpipe (COPY, AUTO_INGEST=TRUE).
• Run a SQL procedure (transform_orders) that cleans data, enforces business rules, and writes into a curated table.
• Publish the result to a Snowflake‑managed Iceberg table: Parquet files + open Iceberg metadata, so Spark/Trino/e6data can query the very same snapshots (no lock‑in).
• Perform row‑count & diff checks to keep curated and Iceberg tables in sync.
• Schedule the whole thing with a daily Snowflake Task (cron 0 2 * * * UTC).
Bonus details: complete DDL/SQL snippets, discussion of Iceberg’s ACID snapshots & time‑travel, and why an open catalog lets you mix query engines without duplicating data. Great reference if you’re weighing open‑format lakehouse patterns inside Snowflake.
Vector and semantic search let lakehouse teams unlock 80% of enterprise data that lives in free-form text, images, and other unstructured formats. By embedding high-dimensional vectors directly into the SQL optimizer, e6data’s “Unify, Don’t Migrate” engine runs cosine-similarity joins faster without extra ETL or a separate vector database. The result: lower costs, simpler pipelines, and far richer insights from customer voice, support chats, docs, and more.
