Which is easier to manage, Redshift or BigQuery?

BigQuery generally has a lower operational footprint because there is no cluster to size or tune. A provisioned Redshift cluster carries more administration, though Redshift Serverless narrows this difference considerably.

Do both support standard SQL?

Yes, both are queried with SQL and are designed for the same kind of analytical, aggregate-heavy workload rather than fast individual-record transactions.

Which is cheaper, Redshift or BigQuery?

It depends heavily on workload shape and each provider’s current pricing, which changes over time and differs by pricing model (cluster-based versus data-scanned). Compare current pricing pages for your specific workload rather than relying on a general answer.

Can I move data between Redshift and BigQuery?

Yes, though it means moving data across clouds, which typically requires an export/import or ETL step and incurs data-transfer costs — there is no native, automatic link between the two warehouses.

Cloud basics

Amazon Redshift vs Google BigQuery: what is the difference?

By the SaveMyCert Editorial TeamPublished 17 Sept 2026Updated 17 Sept 2026

Amazon Redshift and Google BigQuery are both cloud data warehouses built for running SQL analytics over large volumes of data, and they solve the same problem from two different starting points. BigQuery is fully serverless — there is no cluster to size or manage, you simply run queries and Google Cloud handles the underlying compute. Redshift has traditionally been cluster-based, where you provision a set of nodes sized for your workload, though it now also offers a serverless option that narrows this gap considerably. Neither approach is simply better than the other; it is an architectural difference with real trade-offs, not a verdict on which warehouse is more capable. This guide sets out what each service is, where they genuinely diverge, and how to think about choosing between them.

The shared model: the cloud data warehouse

Our what is a data warehouse explainer covers the underlying concept: a system built specifically for querying and analysing large amounts of structured data with SQL, typically using a columnar storage format that makes scanning and aggregating big datasets fast. Both Redshift and BigQuery fit this model, and both are aimed at the same kind of workload — business intelligence dashboards, large aggregate queries, reporting over historical data — rather than the fast, individual-record lookups a transactional database handles. See our data lake vs data warehouse explainer for how a warehouse differs from the raw-storage layer that often feeds it.

What Amazon Redshift does

Amazon Redshift is AWS’s data warehouse — see our what is Amazon Redshift explainer for detail. Its traditional model provisions a cluster of nodes sized to your workload, which you scale up or down as demand changes; Redshift Serverless is a newer option that removes the need to manage cluster sizing directly, running queries and scaling compute automatically instead.

What Google BigQuery does

Google BigQuery is Google Cloud’s data warehouse — see our what is Google BigQuery explainer for detail. It has been serverless from the outset: there is no cluster to provision or size at all, and you interact with it purely by running queries and, in some pricing models, loading data, while Google Cloud manages all of the compute behind the scenes.

Where they actually differ

The architectural starting point of each service shapes most of the practical differences:

Provisioning model — Redshift’s traditional mode asks you to size and manage a cluster (with a serverless option now available); BigQuery has never required cluster sizing, so there is nothing to provision before you run your first query.
Operational overhead — a provisioned Redshift cluster benefits from tuning (distribution keys, sort keys, workload management) that an administrator typically owns; BigQuery removes much of this tuning surface, though large or complex workloads still benefit from schema and query design choices.
Pricing model — both are consumption-based in spirit, but the exact unit differs (compute time/cluster size for Redshift’s provisioned mode versus data scanned or slot-time for BigQuery), and specific rates change over time, so check each provider’s current pricing documentation rather than comparing numbers here.
Ecosystem integration — Redshift integrates naturally with the rest of AWS’s analytics stack (S3, Glue, QuickSight); BigQuery integrates naturally with Google Cloud’s (Cloud Storage, Dataflow, Looker).
Separation of storage and compute — both have moved towards decoupling storage from compute so each can scale independently, though the two arrived at that design from different starting architectures.

When to choose each

Choose Amazon Redshift when your data and analytics pipeline already live on AWS, or when your team wants more direct control over cluster sizing and tuning for a predictable, steady workload. Choose Google BigQuery when your data is on Google Cloud, or when you would rather not think about cluster capacity at all and are comfortable with a pay-for-what-you-query model. Many organisations end up choosing based on which cloud already holds their data, since moving large datasets between clouds for analytics is itself a cost and an engineering task worth avoiding where possible.

It depends on your stack, not a winner

Both services are mature, widely used warehouses capable of running serious analytics workloads at scale, and the gap between "cluster-based" and "serverless" has narrowed as Redshift has added its own serverless option. The more durable way to compare them is by architecture and operating model, as this guide has, rather than by looking for a single best answer — the right choice is the one that fits the cloud, pipeline and team you already have.

Where this appears in cert study

Redshift is core material in AWS Data Engineer Associate, where warehouse design choices are a recurring theme. BigQuery and the broader Google Cloud analytics stack appear in GCP Associate Cloud Engineer, and warehouse concepts more generally are covered in Fabric Data Engineer Associate.

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Questions, answered

They solve the same problem — SQL analytics over large datasets — but from different architectures. BigQuery is fully serverless with no cluster to manage; Redshift is traditionally cluster-based, though it now also offers a serverless option.