LakehouseOne query. Every source. Zero pipelines.
Your stack became a
dependency graph nobody owns.
Airflow orchestrates Spark which writes to S3 which Glue catalogs which dbt reads from Snowflake which Looker queries. Then Kafka happened. Then the ML team needed features. Now you have 23 tools and a 7-figure invoice.
↑ scroll up to watch it collapse ↑
One Lakehouse node. Every source. Every engine. One catalog.
Technical proof.
Human payoff.
Each layer opens like a drawer — showing the SQL that makes it real and the outcome that makes it worth it.
One table format. Every engine.
Iceberg, Delta, and Hudi tables coexist on the same object store. Query them with Spark, Trino, or plain SQL — no format lock-in, no data copies.
-- All formats. One namespace.SHOW TABLES IN prod;-- ┌─────────────────────┬─────────────┐-- │ table │ format │-- ├─────────────────────┼─────────────┤-- │ events │ iceberg │-- │ transactions │ delta │-- │ user_profiles │ parquet │-- └─────────────────────┴─────────────┘
Teams cut storage costs 60% by eliminating warehouse replicas.
Define once. Enforced everywhere.
Tag-based row filters and column masks defined once in the catalog, enforced across every engine. GDPR, HIPAA, SOC 2 — not bolted on.
ALTER TABLE prod.transactionsADD COLUMN MASK credit_cardUSING POLICY pii.mask_card_numberFOR ROLE analyst;-- Enforced in Spark, Trino, dbt
Audit trails auto-generated. Compliance reviews take hours, not weeks.
Share live data. No ETL. No replicas.
Delta Sharing lets you send live table snapshots to any external consumer — partner, vendor, subsidiary — without moving a byte.
CREATE SHARE partner_feedADD TABLE prod.aggregated_metricsPARTITION BY region = 'us-east-1';-- Partner queries live data, zero lag
Eliminated 14-hour nightly ETL jobs for 3 downstream partners.
Feature store. Already there.
Register features directly against lakehouse tables. Online serving pulls from the same versioned snapshot your training pipeline used — no skew.
REGISTER FEATURE user_purchase_freqFROM prod.unified_eventsWINDOW '30d'AGG count(*) / 30.0AS FLOAT ONLINE;-- Training and serving: same data
Feature pipeline from raw to production in 2 hours vs 2 sprints.
The migration script ran clean
on the first run.
Numbers from teams who replaced their data stack with Lakehouse in the last 6 months.
We had 23 dbt models duplicated across two repos because nobody cataloged the originals. Lakehouse's schema registry surfaced every duplicate in the first scan. We deleted 6,000 lines of SQL in week one.
Our Snowflake invoice hit $1.1M last quarter. After migrating the cold query layer to Lakehouse with Iceberg on S3, we're projecting $380K this year. The migration script ran clean on the first attempt.
I manage ML feature pipelines for a 40-person data science org. Feature skew between training and serving was our #1 incident source. With native feature serving from the same lakehouse snapshot, we've had zero skew incidents in 90 days.
That exhale when the
migration finishes clean.
No cluster configs. No Terraform. No seven-figure invoice waiting at the end of the month. Just a governed, versioned, unified lakehouse — in 90 seconds.
Free tier includes 100GB storage · 10B rows/month · Unlimited schemas