Amazon Redshift
vs BigQuery
For data teams managing Redshift clusters and evaluating whether BigQuery's serverless model better fits their analytics workload, the architectural differences between provisioned clusters and on-demand compute determine cost efficiency, operational overhead, and analytical agility.
Side-by-Side Comparison
Provisioned clusters with node types (RA3, DC2). Cluster sizing, node count, and scaling policies are manual decisions. Resize operations require planning. Concurrency scaling adds burst capacity.
Serverless — no clusters, nodes, or sizing decisions. Compute allocated per-query automatically. On-demand or slot reservations. Zero operational overhead for infrastructure management.
Cluster-based pricing — pay for nodes whether queries run or not. Reserved instances for cost reduction (1-3 year commits). Redshift Serverless available but at premium per-RPU pricing.
On-demand: $6.25 per TB scanned. Only pay for queries that run. Slot reservations (flat-rate) for predictable workloads. No charge for idle time. Cost directly tied to usage.
Massively parallel processing (MPP) architecture. Performance depends on cluster size, distribution keys, sort keys, and data organization. Well-tuned Redshift is extremely fast but tuning requires expertise.
Dremel-based execution engine with automatic optimization. No distribution or sort keys to manage. Performance is consistent without manual tuning. Slot reservations provide guaranteed capacity for critical queries.
COPY command from S3. Spectrum for querying S3 data in place. Streaming via Kinesis Data Firehose. Tight integration with AWS data ecosystem (Glue, Lake Formation, EMR).
Batch loading from Cloud Storage. Streaming via Storage Write API. Federated queries to Cloud SQL, Spanner, and Cloud Storage. BigQuery Data Transfer Service for SaaS data sources.
PostgreSQL-based SQL dialect. Compatible with PostgreSQL drivers and tools. Stored procedures in PL/pgSQL and Python. Broader tool compatibility due to PostgreSQL wire protocol.
GoogleSQL (Standard SQL) dialect. JavaScript and SQL UDFs. SQL stored procedures. BigQuery-specific functions (APPROX_COUNT_DISTINCT, ML.PREDICT). Less driver compatibility than PostgreSQL protocol.
Redshift ML uses SageMaker for model training — SQL interface, but models train in SageMaker. Requires SageMaker configuration. ML is an add-on, not native.
BigQuery ML — CREATE MODEL syntax trains models directly in BigQuery. Logistic regression, random forest, boosted trees, time series, and LLM inference. ML is native to the query engine.
Concurrency scaling adds temporary clusters for query bursts. WLM (Workload Management) for query prioritization. Manual intervention for capacity planning. Scaling has latency.
Automatic concurrency scaling for on-demand queries. Slot autoscaling for reservations. No concurrency limits on on-demand pricing. Scaling is invisible and instant.
When BigQuery replaces Redshift effectively
Migrate to BigQuery if Redshift cluster management (sizing, scaling, vacuum, distribution keys) consumes data engineering time that should be spent on analytics, the workload is bursty and paying for idle cluster capacity is wasteful, in-database ML (BigQuery ML) would simplify the ML pipeline by eliminating SageMaker orchestration, or the organization is consolidating on GCP and Redshift is the last remaining AWS service.
Stay on Redshift if the data ecosystem is deeply integrated with AWS services (Glue, Lake Formation, SageMaker, Kinesis) and migration would require rebuilding the entire data pipeline, PostgreSQL compatibility is important for existing tools and drivers, consistent sub-second query latency on well-tuned workloads justifies the tuning investment, or reserved instance pricing makes Redshift cost-competitive for steady-state workloads.
The migration is most compelling for teams where operational overhead is the primary pain point. BigQuery eliminates an entire category of work (cluster management, vacuum operations, distribution key optimization) that Redshift requires for optimal performance.
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