6smarketersSoftware
List your software
Home/Data & Analytics/Data Warehouse/Google BigQuery

Google BigQuery

Data Warehouse

Visit websiteCompare
★★★★☆ 4.5 / 5 on G2 · 4.6 on Capterra (1,223 G2 reviews)
4.5 / 5
G2 rating, 1,223 reviews [source]
Serverless
No infra [source]
Google Cloud
Native [source]
Usage
Based pricing [source]

Quick facts

CategoryServerless cloud data warehouse
VendorGoogle Cloud
Starts atUsage based (storage + queries)
Best forServerless analytics
DeploymentGoogle Cloud
Free planFree tier
IntegrationsGoogle Cloud, BI, ETL
G2 rating4.5 / 5
Capterra rating4.6 / 5

What is Google BigQuery?

Google BigQuery is Google Cloud's serverless data warehouse, with no infrastructure to manage, fast queries on massive data, and tight Google Cloud and AI integration, suited to teams wanting serverless, scalable analytics. It is fully serverless, you load data and run SQL queries without managing infrastructure or clusters, and it automatically scales to query terabytes or petabytes fast, charging for storage and the data your queries scan (or by capacity), with tight integration into Google Cloud (and tools like Looker, Data Studio), built in machine learning (BigQuery ML) and AI (Gemini). Widely used for large scale analytics, especially by Google Cloud organisations, BigQuery is a leading serverless warehouse. For serverless analytics, BigQuery is a leading choice.

BigQuery's distinction is being fully serverless, fast and Google Cloud native. With no infrastructure to manage, it scales automatically to query massive data quickly, so teams analyse huge datasets without operating clusters, and it integrates tightly with Google Cloud and adds built in ML and AI, which non serverless or non Google warehouses do not match for serverless ease on Google Cloud. That serverless architecture, speed and Google integration set it apart.

BigQuery at a glanceGoogle BigQuery is Google Cloud's serverless data warehouse with fast queries on massive data [Google Cloud]. It holds a 4.5 out of 5 rating across 1,223 reviews on G2 and 4.6 on Capterra, a leading choice for serverless analytics.

Who Google BigQuery is best for

Google BigQuery is best for teams and organisations, especially on Google Cloud, that want serverless, scalable analytics on large data without managing infrastructure, with built in ML and AI. It suits serverless analytics. It is a weaker fit for those wanting multi cloud neutrality and data sharing, where Snowflake fits, a unified lakehouse and AI, where Databricks fits, the AWS ecosystem, where Redshift fits, or the Microsoft ecosystem, where Synapse or Fabric fit.

Key features

The core strengths are a fully serverless architecture (no infrastructure to manage), automatic scaling and fast queries on massive data, usage based pricing (by data scanned or capacity), tight Google Cloud integration, built in machine learning (BigQuery ML) and AI (Gemini), and data sharing and governance. BigQuery offers serverless, scalable analytics. Its serverless ease, speed and Google integration are its defining advantages.

Google BigQuery pricing

BigQuery is usage based: you pay for storage and for queries, either on demand (by the amount of data scanned) or with capacity based (slot) pricing, plus a free tier. So costs depend on data volume and query patterns, and unoptimised queries can be costly (partitioning and optimisation help). Confirm details on the BigQuery pricing page.

Integrations and ecosystem

BigQuery integrates with Google Cloud services, BI tools (Looker, Looker Studio, Tableau, Power BI), ETL/ELT tools (dbt, Fivetran), and data science and AI tools, with built in ML. Its Google Cloud integration and BI and data ecosystem, which make serverless analytics easy within Google and beyond, support its positioning.

BigQuery and serverless

Being fully serverless, fast and Google Cloud native is the signature strength: with no infrastructure to manage, BigQuery scales automatically to query massive data quickly, so teams analyse huge datasets without operating clusters, with tight Google integration and built in ML and AI, which non serverless or non Google warehouses do not match. For serverless analytics, this is the main reason to choose BigQuery.

Ease of use and support

BigQuery is highly rated for serverless ease, speed, scalability and Google integration, with Google Cloud support, though reviewers note costs can rise with unoptimised queries and a learning curve for optimisation. Its G2 reviews praise serverless speed and ease, with cost management the main consideration.

Is Google BigQuery worth it?

For teams and organisations, especially on Google Cloud, that want serverless, scalable analytics on large data without managing infrastructure, BigQuery is an excellent choice. The main caution is usage based costs needing query optimisation, so multi cloud and sharing suit Snowflake, a lakehouse Databricks, AWS Redshift, and Microsoft Synapse or Fabric, but for serverless analytics BigQuery leads.

Pricing

On demand

Usage
by data scanned
  • Serverless queries
  • Pay per query
  • Flexible

Capacity (slots)

Usage
by slots
  • Predictable compute
  • Reserved capacity
  • Steady workloads

Storage

Usage
per TB
  • Cheap storage
  • Separate from compute
  • Scale

Pros and cons

Pros

  • Fully serverless (no infrastructure)
  • Fast queries on massive data
  • Automatic scaling
  • Tight Google Cloud integration
  • Built in ML (BigQuery ML) and AI

Cons

  • Costs rise with unoptimised queries
  • Best within Google Cloud
  • Query optimisation learning curve
  • Egress and lock in considerations

Google BigQuery alternatives

These data warehouse tools come up most often against Google BigQuery.

Snowflake

Cloud data platform with separated storage and compute

Databricks

Lakehouse platform unifying data warehousing and AI

Amazon Redshift

AWS petabyte-scale cloud data warehouse

Google BigQuery FAQ

What is Google BigQuery used for?
BigQuery is used as a serverless cloud data warehouse: loading and analysing large volumes of data with SQL and no infrastructure to manage, scaling automatically to query terabytes or petabytes fast, with Google Cloud integration and built in ML and AI, for large scale analytics.
How much does BigQuery cost?
BigQuery is usage based: you pay for storage and for queries, either on demand (by data scanned) or capacity based (slots), plus a free tier. Costs depend on data volume and query patterns. See the BigQuery pricing page.
Is BigQuery serverless?
Yes. BigQuery is fully serverless, meaning there is no infrastructure or clusters to manage; you load data and run queries, and it automatically scales to handle massive datasets fast, which is a defining strength and removes operational overhead.
BigQuery vs Snowflake, which is better?
Google BigQuery is Google Cloud's fully serverless warehouse, while Snowflake is a multi cloud data platform with elastic compute and data sharing. BigQuery suits serverless analytics on Google Cloud; Snowflake suits multi cloud, vendor neutral warehousing and sharing.
Does BigQuery have machine learning?
Yes. BigQuery includes BigQuery ML, letting you build and run machine learning models using SQL directly in the warehouse, plus integration with Google's AI (Gemini), so teams can do ML and AI on their data without moving it out.
Is BigQuery good for large datasets?
Yes. BigQuery is designed for fast analytics on very large datasets (terabytes to petabytes), scaling automatically without managing infrastructure, which is a core reason organisations with big data analytics needs choose it, especially on Google Cloud.

External links