Snowflake
Recent items mentioning Snowflake across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Recent activity highlights Databricks' competitive edge and integration with Snowflake. dbt Labs was named Snowflake Data Integration Product Partner of the Year 2, while Databricks users are actively discussing migrating from Snowflake to Databricks, with one user reporting a 77% cost reduction after a 12-week migration 48. Community discussions also cover ingesting data from Snowflake to Databricks 1 and general comparisons between the two platforms 610.
Generated daily from the 10 most recent items mentioning Snowflake. Click any [N] to jump to the source.
Ingest data from snowflake to databricks
dbt Labs Named Snowflake Data Integration Product Partner of the Year
dbt Labs was named Snowflake Data Integration Product Partner of the Year. This post details dbt Labs' two Snowflake Partner honors, including the CoCo Adoption Award.
What we announced at Snowflake Summit and why it matters
dbt State, dbt Wizard, dbt Core v2.0, and the Fivetran merger
Snowflake to Databricks Migration in 12 weeks and cut cost per run by ~77%. AMA.
Lovelytics wrapped up a Snowflake-to-Databricks migration; 847 DBT models, 35 Info Mart tables, \~77% lower cost per run on a 2XL warehouse. **TL;DR What helped:** * Treated the migration as engineering, not translation. Each dbt model was tested in isolation, not just row counts vs Snowflake. * Routing macro to resolve cross-layer references at runtime, so the same codebase could read from Snowflake, federated Snowflake, and Unity Catalog without forking logic. * Dual model trees in one repo, which let the migration stay in lockstep with live Snowflake changes. * Script-generated wave selectors enabled parallel builds while preserving dependency order. * Used reference-slice validation subsets vs. waiting on full mart refreshes. **TL;DR Cost reduction:** * Reworked joins to use narrow staging dimensions instead of wide marts where possible. * Added incremental predicates to reduce MERGE target scans. * Split wide models into parallel sub-models where the dependency graph allowed it. * Copied static reference data into Delta instead of repeatedly reading it through federation. * Loaded static copies into Delta rather than reading via federation (predicate pushdown is poor). Happy to go into the gotchas: HASH() not being portable, Snowflake MERGE tolerating duplicate keys that Delta doesn't, NULL ordering, and timestamp handling. AMA [Full Blog Post](https://community.databricks.com/t5/technical-blog/partner-blog-847-models-12-weeks-77-less-inside-r1-s-snowflake/ba-p/157284)
NewsBayada’s Snowflake-to-Databricks Migration: Transforming Data for Speed & Efficiency
Delta Lake 3.3.0
Delta Lake 3.3.0 introduces Identity Columns, faster VACUUM LITE, and the ability to enable Row Tracking on existing tables for row-level lineage. It also allows enabling UniForm Iceberg on existing tables without data rewrite and supports Type Widening in Delta Kernel.
