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Head-to-head · Columnar Databases

Google BigQuery vs Exasol

  • Updated Oct 2026
  • Both researched from official sources
  • 1 check side by side
Higher score Google BigQuery #1 in Columnar Databases 9.4/10 Free plan Free plan✓ 0 of 3 features Try Google BigQuery
Exasol #6 in Columnar Databases 8.6/10 Free plan · 30-day trial Free planFree trial✓ 0 of 3 features Visit Exasol

Google BigQuery leads on 0 checks, Exasol on 0, and 1 is even. Who comes out ahead on the 1 yes/no, price and count check where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreGoogle BigQuery · 9.4/10
  • Free planboth
  • Free trialonly Exasol

Google BigQuery scores higher on our rubric for columnar databases: 9.4 against 8.6 out of 10; our editors rank them #1 and #6.

On deployment model, Exasol gives you Hybrid where Google BigQuery offers Cloud.

Google BigQuery is the better fit for serverless analytics and lakehouse teams. Exasol is the better fit for managed and self-hosted enterprise warehousing.

  • Google BigQuery fits best

    Serverless analytics and lakehouse teams

  • Exasol fits best

    Managed and self-hosted enterprise warehousing

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 ↗ Exasol 8.6/10 Visit ↗
At a glance
Editor score 9.4 8.6
Ranking #1 in Columnar Databases #6 in Columnar Databases
Best for Serverless analytics and lakehouse teams Managed and self-hosted enterprise warehousing
Pricing model Free Free plan + paid
Starting price Free $25/GB/mo
Free plan ✓ ✓
Free trial — 30 days
Deployment Cloud Cloud, Self-hosted, Desktop
Platforms Web Web, macOS, Linux
Support Docs, Live chat, Phone, Tickets, Community Email, Phone, Tickets, Community, Docs · 24/7
Compliance SOC 2, ISO 27001, HIPAA ISO 27001, GDPR, PCI DSS
Integrations 10 integrations 14 integrations
Built for Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Google BigQuery 0/3 · Exasol 0/3
Managed service Not checked yet Not checked yet
Object storage support Not checked yet Not checked yet
Streaming ingestion Not checked yet Not checked yet
Specs
Deployment model Cloud Hybrid
Supported data formats Not checked yet Not checked yet
Included storage Not checked yet Not checked yet
Our review
Pros
  • Separates storage and compute for independent scaling
  • Queries lake data and external sources through federated access
  • Combines streaming, SQL-based ML, governance, and AI features
  • In-memory MPP processing supports high-concurrency analytical queries
  • Separate storage and compute with S3 and Azure Data Lake querying
  • Native Python, R, and Java machine-learning model execution
Cons
  • Usage-based pricing varies by data scanned, storage, region, and operation
  • Capacity-based editions require planning around query processing
  • Cloud deployment may not suit teams needing another deployment model
  • Storage costs are billed separately at $25 per TB per month
  • Advanced capacity tiers can reach $158.40 per hour
  • The platform is proprietary rather than open source
Our verdict

Google BigQuery is a fully managed, serverless cloud data warehouse for organizations that store, query, and analyze large datasets. It is aimed at data engineering, business intelligence, data science, real-time analytics, and warehouse…

Read the review →

Exasol is an in-memory, massively parallel analytics database for data warehousing, business intelligence, lakehouse acceleration, and AI/ML workloads. It is aimed at data teams, developers, and enterprises that need high-concurrency SQL…

Read the review →
  1. Google BigQueryColumnar Databases 9.4Free plan
  2. ExasolColumnar Databases 8.6Free plan · 30-day trial

Strengths and trade-offs

  • Google BigQuery — where it wins

    • Separates storage and compute for independent scaling
    • Queries lake data and external sources through federated access
    • Combines streaming, SQL-based ML, governance, and AI features

    Where it doesn't

    • Usage-based pricing varies by data scanned, storage, region, and operation
    • Capacity-based editions require planning around query processing
    • Cloud deployment may not suit teams needing another deployment model

  • Exasol — where it wins

    • In-memory MPP processing supports high-concurrency analytical queries
    • Separate storage and compute with S3 and Azure Data Lake querying
    • Native Python, R, and Java machine-learning model execution

    Where it doesn't

    • Storage costs are billed separately at $25 per TB per month
    • Advanced capacity tiers can reach $158.40 per hour
    • The platform is proprietary rather than open source
  • Google BigQuery9.4/10 · Free plan

    A flexible serverless warehouse for teams combining lakehouse analytics, streaming, and ML.

    Try Google BigQueryFull verdict →
  • Exasol8.6/10 · Free plan · 30-day trial

    A flexible columnar warehouse for demanding analytics, lakehouse, and machine-learning workloads.

    Visit ExasolFull verdict →

More comparisons

All columnar databases comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update