BigQuery vs Redshift

BigQuery and Amazon Redshift are the flagship data warehouses of Google Cloud and AWS respectively. The choice is often dictated by which cloud you live in, but their architectures differ in meaningful ways.

These are the default warehouses of the two largest clouds, and for organizations committed to GCP or AWS the choice is frequently made before the comparison even starts. Still, their architectures reward understanding, because they shape cost, performance, and operations in different ways.

Key Differences

The defining contrast is the compute model. BigQuery is fully serverless: you run queries and Google handles all resource allocation, with no clusters to provision. Redshift began as a provisioned, node-based MPP warehouse and now also offers Redshift Serverless, but its heritage is cluster-based. For teams that want zero infrastructure thinking, BigQuery's serverless model is the more natural fit.

Pricing follows from this. BigQuery charges per byte scanned (on-demand) or via capacity slots, rewarding selective queries and good partitioning. Redshift charges per node-hour for provisioned clusters or per Redshift Processing Unit for Serverless. Neither is universally cheaper; it depends on workload patterns and tuning discipline.

Ecosystem fit is symmetric: BigQuery integrates deeply with Google Cloud and Vertex AI, while Redshift integrates deeply with S3, Glue, IAM, and the broader AWS stack. Both support in-warehouse machine learning, BigQuery ML with SQL and Redshift ML via SageMaker. BigQuery has strong native streaming ingestion; Redshift typically streams via Kinesis and Firehose.

The serverless-versus-provisioned distinction permeates everything. BigQuery's auto-allocated resources mean you never think about cluster sizing, but you do think hard about how much data each query scans, since that drives both cost and speed in on-demand mode. Redshift's provisioned clusters, especially RA3 with managed storage, give steady, predictable performance and cost for consistent workloads, while Redshift Serverless offers a more BigQuery-like experience for spiky ones. Concurrency scales automatically in both, but the mental model and the levers you pull differ.

When to Choose BigQuery

Choose BigQuery if you are on Google Cloud and want a hands-off, fully serverless warehouse. Its pay-per-scan model suits ad-hoc and bursty analytics, its streaming inserts are convenient, and BigQuery ML lets analysts build models in SQL. It minimizes operational overhead.

When to Choose Redshift

Choose Redshift if your platform is AWS-native and you want tight integration with S3, Glue, and IAM. Provisioned clusters give predictable, controllable economics for steady workloads, and existing AWS skills and investments make it a smooth fit.

Streaming and operational ergonomics also separate them. BigQuery's native streaming inserts make near-real-time analytics straightforward, whereas Redshift typically ingests streams through Kinesis and Firehose. Both offer in-warehouse ML, BigQuery ML in SQL and Redshift ML through SageMaker, so the differentiator is rarely capability and more often which cloud already holds your data and identity. Migration friction is also asymmetric: moving a large analytics estate between clouds is expensive in egress and engineering, so the warehouse you choose tends to reinforce the cloud you are on, making the initial platform decision unusually durable.

Verdict

For most organizations, the cloud you already use is the deciding factor. Within that, BigQuery is the more serverless, hands-off option, while Redshift offers deep AWS integration and provisioned cost predictability. Both are mature, high-performance warehouses.