BigQuery vs Redshift: Google vs AWS for Your Data Warehouse (2026)

BigQuery is serverless — you write SQL, Google runs it, you pay for bytes scanned. No clusters, no vacuuming, no capacity planning. Redshift is provisioned — you choose node types, tune sort and distribution keys, handle maintenance. BigQuery wins on zero-ops simplicity. Redshift wins when your team is deep in AWS and wants direct control. The right pick depends on how your team works, not raw benchmarks.

How do the architectures differ?

The split is fundamental: serverless vs. provisioned.

           BigQuery (GCP)
 
  ┌───────────────────────────────────────────┐
  │              Your SQL Query               │
  │                   │                       │
  │          Dremel Query Engine               │
  │        (serverless, auto-scaled)           │
  │                   │                       │
  │     Colossus Distributed Storage           │
  │      (columnar, managed, replicated)       │
  └───────────────────────────────────────────┘
    No nodes. No clusters. You write SQL and pay for what you scan.
 
 
           Redshift (AWS)
 
  ┌───────────────────────────────────────────┐
  │              Your SQL Query               │
  │                   │                       │
  │        Leader Node (query plan)            │
  │            │              │               │
  │     Compute Node 1   Compute Node N       │
  │       (local SSD)      (local SSD)        │
  │            │              │               │
  │     Redshift Managed Storage (S3-backed)   │
  └───────────────────────────────────────────┘
    You size the cluster. Sort keys and dist keys are your responsibility.

BigQuery separates storage and compute completely. Google's Dremel engine spins up thousands of workers behind the scenes for a single query. You don't see them, you don't configure them. Storage sits in Colossus, Google's distributed file system. Upside: you never think about scaling. Downside: you can't force priority when slots run out.

Redshift gives you a cluster. Pick the node type (ra3, dc2, ds2), the number of nodes, and the storage. Define sort keys so filtered columns are physically ordered on disk. Choose distribution keys to control how rows shard across nodes. More work, but the payoff is predictable performance on known query patterns.

Redshift Serverless (2021) blurs this line — removes cluster management, bills per RPU-second. Convenient for variable workloads, but the pricing surprises teams that expected BigQuery-like costs.

How does pricing actually work?

This is where the conversation gets real, because the billing models are fundamentally different.

BigQuery has two pricing tracks. On-demand charges $6.25 per TB scanned (first 1 TB/month free). You pay nothing when nobody's querying. Flat-rate editions (Standard, Enterprise, Enterprise Plus) sell slots — units of compute — starting around $0.04/slot-hour for Standard edition with autoscaling. Flat-rate is cheaper once you pass roughly 40-50 TB scanned per month, but you have to commit.

Redshift provisioned clusters bill per-node-hour. An ra3.xlplus node runs about $1.09/hour on-demand, or ~$0.45/hour with a 1-year reserved instance. You pay whether queries are running or not. Pause/resume exists, but most production clusters stay on. Redshift Serverless charges $0.375 per RPU-hour with a minimum of 8 RPUs — so $3/hour floor even for tiny workloads.

Storage is separate on both. BigQuery: $0.02/GB/month for active storage, $0.01/GB for long-term (90+ days untouched). Redshift Managed Storage on ra3 nodes: $0.024/GB/month. Similar ballpark.

The hidden cost with BigQuery on-demand: someone runs SELECT * FROM events on a 50 TB table. That's a $312 query. Partitioning and column pruning fix this, but new team members will hit it. BigQuery's query validator shows estimated bytes before execution — teach people to read it.

The hidden cost with Redshift: clusters that never get paused. A 3-node ra3.xlplus cluster runs $2,361/month on-demand, whether you're querying at 3am or not. Reserved instances cut this in half, but you're locked in for a year.

How much tuning does each one need?

BigQuery: almost none. Partition your tables by date or another high-cardinality column, cluster on frequently filtered fields, and you're done. The query optimizer handles parallelism, memory allocation, and join strategies automatically. There are no indexes, no distribution keys, no vacuum operations. Performance scales with data size, not with how much time your DBA spent hand-tuning.

Redshift: a lot. Sort keys determine physical row ordering — pick the wrong one and your range-filtered queries do full table scans. Distribution keys control how rows land across nodes — a bad choice means massive data shuffling on joins. VACUUM reclaims space after deletes and restores sort order. ANALYZE updates query planner statistics. These operations are technically automated now (auto vacuum, auto analyze), but they don't always keep up with heavy write loads. You'll still run them manually.

  BigQuery              Redshift
  ────────              ────────
  Write SQL ──────────► Write SQL
       │                     │
  (auto-parallelism)    Check sort/dist keys
       │                     │
  Done ────────────────► Maybe slow? → VACUUM/ANALYZE → re-tune → re-run

BigQuery isn't immune to performance issues — queries that scan too much data are slow and expensive regardless. But the fix is structural (better partitioning, materialized views) not operational (maintenance jobs at 2am).

What about SQL dialect differences?

Both speak SQL, but not the same SQL.

BigQuery uses GoogleSQL (formerly Standard SQL). Redshift uses a PostgreSQL-derived dialect. Vanilla ANSI SQL works on both. The differences emerge with nested data, date functions, and UDFs.

BigQuery handles nested and repeated fields natively — STRUCT and ARRAY types are first-class. UNNEST a JSON array inline. Redshift added SUPER type for semi-structured data, but shredding nested JSON into flat columns is still the common pattern.

Date/time: BigQuery has TIMESTAMP, DATE, TIME, DATETIME (no timezone — a common bug source), and INTERVAL. Redshift leans on PostgreSQL conventions — DATEADD, DATEDIFF, GETDATE(). If your team already writes Postgres, Redshift SQL feels familiar.

UDFs: BigQuery supports JavaScript and SQL UDFs, plus Cloud Functions. Redshift supports SQL and Python UDFs, plus Lambda. Different languages, same concept.

How do they fit into their cloud ecosystems?

This is often the deciding factor. If your company is 90% AWS, Redshift slots in without friction. If you're on GCP, BigQuery is the default. The ecosystem integration goes deep.

  GCP Ecosystem                         AWS Ecosystem
  ─────────────                         ─────────────
 
  Cloud Storage (GCS)                   S3
       │                                     │
  Dataflow / Pub/Sub ────► BigQuery     Glue / Kinesis ────► Redshift
       │                       │             │                   │
  Vertex AI                    │        SageMaker                │
  Looker ──────────────────────┘        QuickSight ──────────────┘
  Cloud Composer (Airflow)              MWAA (Airflow)
  Dataform (dbt-like)                   dbt (community standard)

BigQuery + GCP: Pub/Sub streams into BigQuery subscriptions. Dataflow handles ETL. GCS acts as a staging layer with external table support. Vertex AI pulls training data natively. Looker was born BigQuery-first. Dataform handles SQL transformations inside BigQuery itself.

Redshift + AWS: S3 is the staging and lake layer — Redshift Spectrum queries S3 without loading it. Glue handles ETL and the Data Catalog. Kinesis streams into Redshift via Firehose. SageMaker integrates for ML. QuickSight connects natively.

Cross-cloud access exists but adds cost. BigQuery Omni deploys compute in AWS to query S3 — expensive and feature-limited. Redshift can't query GCS natively. Data in one cloud means the warehouse in that cloud wins.

What about ML capabilities?

Both warehouses want to be your ML platform too. Neither replaces a real one.

BigQuery ML lets you train models with SQL — CREATE MODEL ... OPTIONS(model_type='logistic_reg'). Linear regression, K-means, ARIMA_PLUS time-series, XGBoost, deep neural networks. You can import TensorFlow and ONNX models. For an analyst who knows SQL but not Python, BQML handles basic predictive work without provisioning anything.

Redshift ML delegates to SageMaker Autopilot. You write SQL to define training data, Redshift ships it to SageMaker, SageMaker trains the model, Redshift surfaces predictions via a SQL function. More powerful in theory — SageMaker's full model zoo — but the indirection adds debugging complexity. If Autopilot picks a bad model, troubleshooting means leaving Redshift.

For serious ML, both platforms want you on their adjacent services (Vertex AI, SageMaker). In-warehouse ML is for scoring and simple models.

Side-by-side comparison

How to decide

Pick BigQuery if you want zero infrastructure management. Your team writes SQL and expects queries to just run — no cluster sizing, no vacuum schedules, no sort key debates. You're on GCP or cloud-agnostic. Your workloads are spiky and you'd rather pay per query than keep a cluster warm.

Pick Redshift if your stack is AWS and you want tight integration with S3, Glue, Lambda, and SageMaker. Your queries are predictable enough that a right-sized cluster stays well-utilized. Your team has the expertise (and willingness) to tune sort keys, distribution, and WLM queues for maximum throughput.

One thing worth flagging: Redshift Serverless is not BigQuery. It removes cluster management but doesn't match BigQuery's pricing model or automatic scaling depth. Teams that move to Redshift Serverless expecting BigQuery-style costs often get sticker shock from the RPU minimum.

Where Fastero fits

Fastero connects to both BigQuery and Redshift — plus Snowflake, Postgres, and 15+ other sources. You write queries with natural language or SQL, build dashboards, and set up alerts without switching tools for each data source.

Running BigQuery for marketing analytics and Redshift for your product data? Fastero lets you cross-join across both using DuckDB under the hood — no ETL pipeline, no data movement. The query runs where the data lives, results merge locally.

If you haven't picked a warehouse yet and your data lives in a transactional database, you might not need one at all. Read how to connect multiple databases to one dashboard without a warehouse or build a live KPI dashboard straight from Postgres. And if you're weighing local-first analytics for smaller datasets, DuckDB vs SQLite breaks down that decision.

FAQ

Is BigQuery cheaper than Redshift? For spiky, unpredictable workloads — yes, often dramatically cheaper because you pay nothing when idle. For steady, heavy query loads running 16+ hours a day, a right-sized Redshift reserved cluster can be cheaper per query-hour. The crossover point depends on your scan volume and how disciplined your team is about partitioning.

Can I migrate from Redshift to BigQuery (or vice versa)? Yes, but it's not painless. Schema translation is easy — most SQL maps cleanly. The hard parts: rewriting UDFs, re-targeting ETL pipelines, updating BI connections, and re-tuning performance. Budget 2-4 weeks for a small warehouse, 2-6 months for a large one.

Does Redshift Serverless replace the need for cluster management? It removes node provisioning and auto-scales RPUs, but you still configure VPC networking, manage IAM, and think about sort keys. The pricing — $0.375/RPU-hour with an 8 RPU minimum — means a $3/hour floor even for trivial workloads. Simpler, not simple.

What's BigQuery slot contention? On-demand queries share a slot pool with other GCP customers. During peak hours, your query gets fewer slots and runs slower — no error, just latency. Flat-rate reservations guarantee dedicated slots, which is why high-volume teams move to editions. Monitor slot utilization via INFORMATION_SCHEMA.JOBS.

Can I query data in S3 from BigQuery, or GCS from Redshift? BigQuery Omni queries S3 data by deploying compute in AWS — expensive and feature-limited. Redshift can't query GCS natively. For cross-cloud querying, a federation layer like Fastero queries each source where it lives and merges results.

Which one has better concurrency? BigQuery handles high concurrency better out of the box — serverless architecture auto-scales slots across queries. Redshift uses WLM queues where you configure concurrency and memory per queue (default: 5 concurrent queries). More concurrent queries means less memory each. Concurrency Scaling adds transient clusters for bursts, but at extra cost.

Try Fastero free — connect BigQuery or Redshift in one click and start querying with AI. Cross-join both if you need to. No credit card required.