BigQuery vs Redshift

BigQuery and Redshift are the flagship data warehouses of Google Cloud and AWS respectively. They represent different philosophies: serverless versus provisioned infrastructure.

Architecture Differences

BigQuery is serverless from the ground up. There are no nodes to provision or clusters to manage. Google's Dremel engine processes queries using a distributed tree architecture, reading from Capacitor columnar storage on Colossus. Resources scale automatically per query.

Redshift uses a Massively Parallel Processing (MPP) architecture with a leader node and compute nodes. Data is distributed across nodes using distribution keys. Redshift Serverless (newer) abstracts node management but is architecturally different from BigQuery's native serverless approach.

Performance Characteristics

Both deliver excellent analytical performance on large datasets. Redshift's tuning knobs (distribution keys, sort keys, compression encodings) allow optimization for known query patterns. BigQuery automatically optimizes storage layout and query execution.

Redshift Spectrum extends queries to S3, creating a lakehouse pattern. BigQuery's external tables and BigLake provide similar capabilities for querying data in Cloud Storage without loading.

Trade-offs

BigQuery's serverless model eliminates capacity planning but offers less control over query performance. Redshift's provisioned model requires more management but provides predictable performance and cost for steady workloads.

For data warehouse design, the choice typically follows cloud platform commitment. Migrating between them requires significant effort due to SQL dialect differences and ecosystem dependencies.

Cost Model

BigQuery charges per TB scanned (on-demand) or flat-rate for committed slots. Redshift charges per node-hour, with reserved instances offering up to 75% savings. For predictable heavy workloads, Redshift reserved instances can be cheaper. For variable workloads, BigQuery's on-demand pricing wins.

Real-World Usage

BigQuery powers analytics at Twitter, UPS, and Spotify. Redshift serves Netflix, Nasdaq, and McDonald's. Both handle multi-petabyte analytical workloads.

Learn about data pipeline architecture and ETL patterns. See our system design guide and pricing.