23 апреля 2026 г.

Snowflake vs BigQuery vs Redshift

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Зачем это знать

Cloud data warehouses — foundation modern analytics. Understanding trade-offs важен — вы будете with one of них в new role.

На собесах cloud DWH часто discussed. Analyst должен know basics.

Быстрое сравнение

Snowflake BigQuery Redshift
Vendor Snowflake (multi-cloud) Google AWS
Pricing Pay per compute + storage Pay per query / storage Pay per cluster + storage
Performance Good Fastest typically Depends sizing
Ease Easy Very easy Moderate
Scaling Automatic Automatic Manual cluster resize
Ecosystem Mature marketplace Google integration AWS integration

Snowflake

Pros

  • Multi-cloud: runs AWS, GCP, Azure
  • Separated compute / storage
  • Time-travel (historical data)
  • Semi-structured JSON support
  • Zero copy cloning
  • Marketplace data sharing

Cons

  • Not cheapest
  • Complex features learning curve
  • Multi-tenant — sometimes slow

Use case

  • Enterprise needing cross-cloud
  • Teams wanting managed without too much tuning

BigQuery

Pros

  • Serverless — zero management
  • Extremely fast большие queries
  • Pay per query — no idle costs
  • ML built-in (BigQuery ML)
  • Google ecosystem integration

Cons

  • Google Cloud lock-in
  • Pricing can surprise (scan-based)
  • Less control performance tuning
  • SQL dialect quirks

Use case

  • Google Cloud stack
  • Ad-hoc workloads
  • Need ML in SQL

Redshift

Pros

  • AWS ecosystem tight
  • Mature (first major cloud DWH)
  • Predictable cost (cluster-based)
  • Compatible Postgres syntax mostly

Cons

  • Cluster management required
  • Slow as other options typically
  • Scaling более manual
  • VACUUM / ANALYZE overhead

Use case

  • AWS-heavy stacks
  • Predictable workload
  • Legacy / existing setup

Pricing models

Snowflake

  • Storage: $23/TB/month
  • Compute: warehouse credits (~$2-$4/hour)

Can pause warehouses when не used.

BigQuery

  • Storage: $20/TB/month active, $10/TB archived
  • Compute: $5/TB scanned (on-demand) OR flat rate slots

Scan-based unpredictable.

Redshift

  • Cluster: dedicated nodes, ~$0.25-$6/hour
  • Storage: included в node
  • Pause available (stops billing)

Predictable but uses idle capacity.

SQL dialects

Snowflake SQL

Similar к ANSI SQL. Few Snowflake-specific (QUALIFY).

BigQuery SQL (Standard)

Similar. Some quirks (STRUCT, ARRAY).

Redshift SQL

Closest к Postgres. Familiar ease of transition.

Performance

Small queries

All fast (seconds).

Large queries

BigQuery often fastest. Parallelism natural.

Repeated queries

Snowflake cache helps. BigQuery auto-cache.

Concurrent users

Snowflake separate warehouses (isolation). BigQuery shared (slot-based).

Features сomparison

Time travel

Snowflake: 90 days default (restore deleted data).

BigQuery: 7 days default.

Redshift: no built-in. Backups manual.

Zero-copy clone

Snowflake: instant clones.

BigQuery: similar (clone tables).

Redshift: deep copy (expensive).

Security / governance

All decent. Enterprise features similar.

ML

BigQuery: ML в SQL (CREATE MODEL). Unique.

Snowflake: Snowpark (Python / Java runtime).

Redshift: Redshift ML via SageMaker.

Semi-structured (JSON)

Snowflake: excellent (VARIANT type).

BigQuery: native JSON / STRUCT / ARRAY.

Redshift: SUPER type (newer).

Для analyst

Tools ecosystem

All support:

  • dbt
  • Tableau / Looker / Metabase
  • Python libraries
  • Airflow / orchestrators

Pretty equal.

Learning

Transferable SQL skills. Main changes:

  • Dialect quirks
  • Specific functions
  • Cloud tooling

Russian context

Availability

Cloud DWH sometimes limited в Russia due к sanctions / payments.

Alternatives:

  • ClickHouse (own infrastructure)
  • Yandex Cloud (DataSphere, YDB)
  • Arenadata (domestic)

For now, many Russian companies — ClickHouse / own stacks.

If accessing

Via other countries (Uzbekistan / UAE entities) — some work.

Migration pain

Between DWH:

  • SQL differences
  • Data transfer
  • Tool reconfiguration

Avoid switching unless major benefit.

Choosing

Startup

BigQuery — serverless, quick start. Or Snowflake if multi-cloud future.

Enterprise

Depends existing stack. Avoid radical changes.

Specific needs

  • ML в SQL → BigQuery
  • Multi-cloud → Snowflake
  • Predictable costs → Redshift (or Snowflake w/ planning)

Common misconceptions

«All cloud DWH равны»

Differ в pricing model, performance, features.

«Cheaper always better»

Performance matters. $1k savings vs 10x slower queries.

«On-prem обязательно dying»

Some use cases on-prem fine (large stable, regulatory, cost predictability).

«ClickHouse same as DWH»

Different product. OLAP, яet different trade-offs.

На собесе

«Experience с какой?»

Honest. If one — don't pretend know all.

«BigQuery vs Snowflake?»

Trade-offs. Depends use case.

«Why one over another?»

Concrete reasons (cost, performance, features).

Learning

Free tiers

  • Snowflake: $400 trial credits
  • BigQuery: 1 TB query / 10 GB storage free monthly
  • Redshift: 2 months free trial (AWS)

Use trial. Practice.

Courses

  • Each vendor trainings
  • Coursera / A Cloud Guru

Связанные темы

FAQ

Need learn все?

No. One well. Transferable concepts.

Russia какой?

ClickHouse / Yandex options practical.

Best performance?

Depends workload. Benchmarks vary. BigQuery often wins serverless.

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