Cloud Data Warehouses

Why This Page Covers Multiple Vendors

A data engineer's real job is choosing and operating a data warehouse — not memorizing one vendor's SQL dialect. This page deliberately compares BigQuery, Redshift, Synapse, and Snowflake side by side, because that comparison is the actual skill; going deep on one vendor only makes sense once a project has already chosen its stack.

The Landscape, Compared

Warehouse	Provider	Architecture	Standout feature

|---|---|---|---|

BigQuery	Google Cloud	Serverless, fully managed	No cluster management — pay per query/storage
Redshift	AWS	Cluster-based (or Serverless)	Deep AWS ecosystem integration
Synapse Analytics	Azure	Cluster-based + serverless SQL pools	Combines data warehousing + big data in one service
Snowflake	Multi-cloud (runs on AWS/Azure/GCP)	Separates storage & compute completely	True multi-cloud portability, instant compute scaling

Core Concepts That Apply Across All of Them

•Columnar storage — data warehouses store data by column, not row, making aggregate queries (SUM, AVG, GROUP BY) dramatically faster than row-based OLTP databases.

•Separation of storage and compute — most modern warehouses (Snowflake, BigQuery, Redshift Serverless) let you scale query compute independently from stored data volume, so you only pay for compute when actually querying.

•Partitioning & clustering — organizing large tables by date or key columns so queries scan less data — this is the single biggest lever for both cost and speed in every warehouse listed above.

•Materialized views — pre-computed query results that refresh incrementally, used to speed up dashboards hitting the same aggregations repeatedly.

Choosing One

If you need...	Consider

|---|---|

Already deep in GCP, want zero ops	BigQuery
Already deep in AWS ecosystem	Redshift
Already deep in Azure, want warehouse + big data together	Synapse
Multi-cloud portability, or not yet locked into one cloud	Snowflake

Cost Model Basics

•BigQuery/Snowflake/Redshift Serverless: pay primarily for query bytes scanned or compute-seconds + storage.

•Redshift (provisioned)/Synapse (dedicated pools): pay for reserved cluster capacity, running whether queried or not — cheaper at very high, constant utilization; wasteful at low/spiky usage.

Where This Fits in a Data Pipeline

Cloud data warehouses are typically the destination in an ELT pipeline: raw data lands via Airflow/Kafka → gets transformed with dbt → lands in the warehouse for BI tools (Power BI, Tableau, Looker) to query directly.

💡 Vendor-specific deep dives

Once your project has picked a warehouse, going deep on that vendor's specific SQL dialect and performance tuning (e.g. BigQuery's cloud/bigquery page) is exactly the right next step — this page is the "which one, and why" step that should come first.

Cloud Data Warehouses — Overview