Google BigQuery vs Apache Spark

Google BigQuery9.4Visit Apache Spark8.9Visit

Higher score Google BigQuery #1 in Big Data Analytics Software 9.4/10 Free plan Free plan✓ 0 of 1 features Visit Google Cloud

vs

Apache Spark #4 in Big Data Analytics Software 8.9/10 Free plan Free plan✓ 0 of 1 features Visit Apache Spark

• Google BigQuery fits best

  Teams wanting serverless cloud SQL analytics

• Apache Spark fits best

  Teams building flexible distributed data pipelines

Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. Bought places stand at the number bought; this page and our disclosure say so. Paying vendors may get a fuller, more detailed review from our editors, and that review can change their score. How we rank.

Side by side

Feature	Google BigQuery 9.4/10 Visit ↗	Apache Spark 8.9/10 Visit ↗
At a glance
Editor score	9.4	8.9
Ranking	#1 in Big Data Analytics Software	#4 in Big Data Analytics Software
Best for	Teams wanting serverless cloud SQL analytics	Teams building flexible distributed data pipelines
Pricing model	Free	Free
Starting price	Free	Free plan
Free plan	✓	✓
Free trial	—	—
Deployment	Cloud	Self-hosted
Platforms	Web	Windows, macOS, Linux
Support	Docs, Live chat, Phone, Tickets, Community	Community, Docs
Integrations	10 integrations	20 integrations
Built for	Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Google BigQuery 0/1 · Apache Spark 0/1
Built-in visualization	Not checked yet	Not checked yet
Specs
Deployment	Not checked yet	Not checked yet
Processing model	Not checked yet	Not checked yet
Documented data scale	Not checked yet	Not checked yet
Data source connectors	Not checked yet	Not checked yet
Query languages	Not checked yet	Not checked yet
Our review
Pros	Storage and compute scale independently for SQL analytics. Federated queries and external tables connect to Cloud Storage and other sources. BigQuery ML supports model workflows in SQL, alongside streaming and governance controls.	Processes batch and streaming workloads with SQL and DataFrame APIs Supports Python, SQL, Scala, Java, and R interfaces Runs on Standalone, YARN, or Kubernetes and connects to varied data sources
Cons	Costs can vary by region and operation, with separate storage and ingestion charges. Capacity-based editions add a different pricing model alongside on-demand billing. Cloud deployment may not suit teams seeking a non-cloud warehouse.	Self-hosted deployment requires teams to manage their environment Not a managed warehouse or hosted ETL service Support is through community resources and documentation
Our verdict	Google BigQuery is a fully managed, serverless cloud data warehouse for organizations that need to store, query, and analyze large datasets. It is aimed at data engineering, business intelligence, data science, and real-time analytics… Read the review →	Apache Spark is an open-source distributed computing engine for large-scale data processing and analytics. Developers, data engineers, analysts, and organizations processing data across clusters can use it for batch workloads, incremental… Read the review →

Strengths and trade-offs

• Google BigQuery — where it wins

  • Storage and compute scale independently for SQL analytics.
  • Federated queries and external tables connect to Cloud Storage and other sources.
  • BigQuery ML supports model workflows in SQL, alongside streaming and governance controls.

  Where it doesn't

  • Costs can vary by region and operation, with separate storage and ingestion charges.
  • Capacity-based editions add a different pricing model alongside on-demand billing.
  • Cloud deployment may not suit teams seeking a non-cloud warehouse.

• Apache Spark — where it wins

  • Processes batch and streaming workloads with SQL and DataFrame APIs
  • Supports Python, SQL, Scala, Java, and R interfaces
  • Runs on Standalone, YARN, or Kubernetes and connects to varied data sources

  Where it doesn't

  • Self-hosted deployment requires teams to manage their environment
  • Not a managed warehouse or hosted ETL service
  • Support is through community resources and documentation

• Google BigQuery9.4/10 · Free plan

  A flexible SQL analytics warehouse with separate storage and compute and usage-based pricing.

  Visit Google CloudFull verdict →
• Apache Spark8.9/10 · Free plan

  Flexible distributed processing for teams building batch and streaming data pipelines.

  Visit Apache SparkFull verdict →