Alternatywa tylko UE dla Snowflake.
Snowflake is the cloud data warehouse that won the analytics market through separation of compute and storage, with EU regions on AWS, Azure or GCP. Snowflake Inc. is a Delaware corporation; the EU regions live on US-hyperscaler infrastructure — meaning two layers of US jurisdiction. For analytics workloads on EU customer data, Schrems II compliance is genuinely difficult on Snowflake. The sovereign alternatives are: ClickHouse (open-source columnar warehouse), DuckDB (embedded analytics), or PostgreSQL with appropriate columnar extensions — all deployable on EU sovereign infrastructure.
"Region UE" to nie suwerenność. Decydują cztery pytania.
Rezydencja danych mówi, gdzie znajdują się bity. Suwerenność mówi, który system prawny może wymusić dostęp. Odpowiedź musi być spójna we wszystkich czterech — w przeciwnym razie stack nie jest suwerenny.
Gdzie dane są fizycznie przechowywane?
Nie "w chmurze" — które centrum danych, w którym kraju, pod którą jurysdykcją.
Kto jeszcze znajduje się w Państwa ścieżce danych?
Każdy dostawca dotykający danych: CDN, przekaźnik e-mail, tracker błędów, pipeline analityczny.
Czyje prawa mogą wymusić ujawnienie?
Dostawca z siedzibą w USA podlega FISA 702 i ustawie CLOUD Act — nawet gdy dane znajdują się we Frankfurcie.
Kto faktycznie ma klucze szyfrujące?
Jeśli dostawca chmury ma zarówno dane, jak i klucze, może je odczytać — niezależnie od DPA.
Nie spełnia kryterium jurysdykcji i depozytu kluczy.
Bity w UE, spółka matka w USA, podprzetwarzający z USA w domyślnej ścieżce, klucze zarządzane przez dostawcę.
Spełnia wszystkie cztery kryteria.
Hostowane w UE na infrastrukturze z siedzibą europejską. Zero podprzetwarzających z USA w domyślnej ścieżce. Klucze klienta lub europejskiego KMS. Wymienieni z nazwy w Państwa DPA z Artykułu 28.
Dlaczego zespoły wychodzą Snowflake
Snowflake exits we have scoped come from regulated workloads where the analytics warehouse holds personal data of EU customers, and the Schrems II analysis fails on multiple layers. The unique migration challenge: data warehouses are large, queries are complex, and dbt / Looker / Tableau pipelines need re-pointing. The honest answer for a Snowflake exit is 3-6 months of careful work, not a quick swap. Where the savings are: Snowflake credits at scale ($20k-100k+/month is common) compress to ClickHouse on EU bare metal at a fraction.
Snowflake usługi i ich odpowiedniki tylko z UE
Migracja to nie "zamiana jednej skrzynki na drugą". Poniższe mapowanie jest tym, co uruchamiamy dla klientów opuszczających Snowflake z powodów Schrems II — pełna jurysdykcja UE, brak amerykańskiej spółki matki w ścieżce danych.
| Snowflake usługa | Alternatywa tylko UE | Notatka inżynierska |
|---|---|---|
| Snowflake compute (warehouses) | ClickHouse on EU compute (Hetzner dedicated, OVH bare metal), self-managed Trino on EU | ClickHouse is the strongest sovereign alternative for OLAP workloads. For ad-hoc query workloads, Trino over EU object storage is the lakehouse pattern. |
| Snowflake storage | OVH Object Storage, Wasabi EU as data lake, ClickHouse internal storage on EU NVMe | For lakehouse architecture, EU S3-compatible storage as the data layer with ClickHouse or Trino as the query engine. |
| Snowpipe (continuous ingestion) | ClickHouse Kafka engine, custom ingestion via Apache Airflow on EU, dbt-cloud-replacement self-hosted | For Kafka-based ingestion, ClickHouse has native Kafka engine. For batch ingestion, Airflow on EU compute. |
| Streams & Tasks | Apache Airflow on EU, ClickHouse materialized views, Postgres triggers + LISTEN/NOTIFY | Materialized views in ClickHouse cover most "Stream" use cases. |
| Snowpark (Python/Scala in DB) | PySpark on EU compute, ClickHouse Python UDFs, dbt models with Python | For ML and feature engineering at the warehouse layer, PySpark on EU compute is the standard pattern. |
| Time Travel + Zero-Copy Cloning | ClickHouse table snapshots, PostgreSQL pg_dump + restore, application-layer point-in-time queries | Snowflake's Time Travel is a unique feature; ClickHouse snapshots provide a rougher equivalent. |
| Secure Data Sharing | Bring-your-own-key encrypted exports to EU object storage, custom API layer for shared datasets | Secure Data Sharing has no direct equivalent; the migration involves redesigning the data-sharing pattern. |
| Snowflake Marketplace | Direct vendor relationships for any third-party data, EU-hosted data marketplaces (limited maturity) | For datasets you currently subscribe to via Marketplace, direct vendor contracts are typically required. |
| Snowflake Cortex (LLMs) | Mistral AI (FR), Aleph Alpha (DE), self-hosted Llama on EU GPUs | Cortex is recent; the sovereign EU LLM space (Mistral, Aleph Alpha) has matured to be a real alternative. |
| BI tool integrations (Tableau, Looker, dbt Cloud) | Same BI tools repointed to ClickHouse / Trino, Metabase self-hosted, dbt Core (open-source) on EU runners | The BI tool layer typically transfers cleanly with new connection strings; dbt Cloud → dbt Core on self-hosted EU CI. |
Jak migrujemy z Snowflake
Typowa migracja segmentu mid-market przebiega w trzech fazach. Poniższe liczby zakładają zespół inżynierski 6-10 osób i umiarkowanie złożony stack aplikacyjny.
Architecture decision + audit
Decide ClickHouse vs Trino+lakehouse vs PostgreSQL based on query patterns and data volume. Inventory every dbt model, every dashboard, every external integration. The architecture decision dominates the schedule.
Pilot + parallel run
Migrate a representative subset of workloads to the EU target. Run parallel for validation. Tune ClickHouse cluster sizing based on real query patterns. dbt models converted (most run unchanged on dbt Core with adapter swap).
Full cutover
Phased migration of remaining workloads. BI tools repointed. Snowflake accounts scoped down. Final cutover with a rollback plan; Snowflake retained for archival access for 60-90 days post-cutover.
5-year TCO on Snowflake → ClickHouse migrations: typically 60-85% cheaper at scale. A team running $50k/month of Snowflake credits often replaces it with €5-10k/month of EU ClickHouse infrastructure plus the managed-partner fee. The break-even point is around $5-10k/month of Snowflake spend; below that, the engineering cost of migration may exceed the saved spend over a 3-year horizon.
Często zadawane pytania
Snowflake has Frankfurt and other EU regions — does that solve GDPR?
No. Snowflake Inc. is US-headquartered (parent jurisdiction), and the EU regions run on AWS/Azure/GCP — also US-headquartered (infrastructure jurisdiction). Two layers of US legal exposure under the CLOUD Act and FISA 702. For Schrems II–strict workloads, neither is acceptable.
Is ClickHouse really comparable to Snowflake?
For OLAP query workloads, ClickHouse is genuinely competitive — often faster on equivalent hardware. The differences: ClickHouse requires more operational expertise, Snowflake's separation of compute and storage is harder to replicate cleanly, and Snowflake's ecosystem (Marketplace, Cortex, etc.) doesn't fully exist on ClickHouse. For pure analytics workloads, the gap is small.
What about ClickHouse Cloud — they have an EU region?
ClickHouse Inc. is a US Delaware corporation. ClickHouse Cloud EU regions run on AWS — same dual US-jurisdiction problem as Snowflake. The sovereign answer is self-hosted ClickHouse on EU compute. Aiven offers managed ClickHouse with a clearer EU-jurisdiction story (Aiven is Finnish).
How does dbt fit in?
dbt Core is open-source and runs anywhere; dbt Cloud is dbt Labs Inc. (US). For sovereign workloads, dbt Core on a self-hosted CI runner (GitLab CI EU, Forgejo Actions) replaces dbt Cloud. The actual dbt models port cleanly with the warehouse adapter swap (snowflake → clickhouse).
How long does a Snowflake exit really take?
For a small-to-mid Snowflake usage ($5-20k/month, dozens of dbt models): 3-6 months elapsed time. For enterprise Snowflake ($50k+/month, hundreds of models, complex data sharing): 9-18 months. Snowflake migrations are not weekend projects — they require planning, parallel runs, and careful BI-layer choreography.
Can we keep some Snowflake and migrate the rest?
Hybrid is sometimes the right answer for very specific Snowflake-only features. The discipline: keep only non-personal-data workloads on Snowflake (e.g. internal analytics on aggregated metrics with no PII), and document the boundary in the DPA. For most regulated workloads, full exit is cleaner than the documentation burden of a hybrid.
Zaplanuj wyjście z Snowflake.
30-minutowa rozmowa zakresowa. Mapujemy Państwa stack względem alternatyw tylko z UE, szacujemy nakład pracy migracji i mówimy, czy to właściwa decyzja.