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Head-to-head · OLAP Software

Google BigQuery vs Apache Pinot

  • Updated Oct 2026
  • Both researched from official sources
  • 4 checks side by side
Higher score Google BigQuery #1 in OLAP Software 9.4/10 Free plan Free plan✓ 0 of 3 features Try Google BigQuery
Apache Pinot #8 in OLAP Software 8.7/10 Open source ✓ 3 of 3 features Visit Apache Pinot

Google BigQuery leads on 0 checks, Apache Pinot on 0, and 4 are even. Who comes out ahead on the 4 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreGoogle BigQuery · 9.4/10
  • Free planonly Google BigQuery
  • Most featuresApache Pinot · 3 of 3

Google BigQuery scores higher on our rubric for olap software: 9.4 against 8.7 out of 10; our editors rank them #1 and #8.

Google BigQuery is the better fit for cloud-native analytics teams. Apache Pinot is the better fit for embedded real-time analytics builders.

  • Google BigQuery fits best

    Cloud-native analytics teams

  • Apache Pinot fits best

    Embedded real-time analytics builders

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Side by side

Feature Google BigQuery 9.4/10 Visit ↗ Apache Pinot 8.7/10 Visit ↗
At a glance
Editor score 9.4 8.7
Ranking #1 in OLAP Software #8 in OLAP Software
Best for Cloud-native analytics teams Embedded real-time analytics builders
Pricing model Free Free
Starting price Free Open source
Free plan ✓ (best) Not checked yet
Free trial — —
Deployment Cloud Self-hosted
Support Docs, Live chat, Phone, Tickets, Community Community, Docs
Integrations 10 integrations 13 integrations
Built for Small business, Mid-market, Enterprise Mid-market, Enterprise
Features Google BigQuery 0/3 · Apache Pinot 3/3
Real-time ingestion Not checked yet ✓ (best)
Row-level security Not checked yet ✓ (best)
Embedded analytics Not checked yet ✓ (best)
Specs
Deployment Not checked yet Self_hosted
Query language Not checked yet Sql
Included users Not checked yet Not checked yet
Data source coverage Not checked yet Streaming: Kafka, Pulsar, Kinesis; batch: Hadoop, Spark, Amazon S3, Azure ADLS, Google Cloud Storage
Our review
Pros
  • Separate, independently scalable storage and compute
  • SQL-based BigQuery ML, vector search, and generative AI functions
  • Federated queries, streaming ingestion, CDC, and broad integrations
  • Streams data from Kafka, Pulsar, and Kinesis in real time
  • Combines streaming and batch sources in one table
  • Supports SQL, REST access, joins, upserts, and varied indexes
Cons
  • Usage-based costs vary by region and operation
  • Additional storage, ingestion, and extraction charges can apply
  • Cloud deployment limits it to web and API-based workflows
  • Self-hosted deployment requires infrastructure ownership
  • No managed cloud service is included
  • Its focus is real-time OLAP rather than general-purpose analytics
Our verdict

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

Read the review →

Apache Pinot is an open-source, distributed OLAP database for user-facing and agent-facing real-time analytics. It is designed for embedded analytics, customer dashboards, metrics APIs, and other high-concurrency analytical applications.…

Read the review →
  1. Google BigQueryOLAP Software 9.4Free plan
  2. Apache PinotOLAP Software 8.7Open source

Strengths and trade-offs

  • Google BigQuery — where it wins

    • Separate, independently scalable storage and compute
    • SQL-based BigQuery ML, vector search, and generative AI functions
    • Federated queries, streaming ingestion, CDC, and broad integrations

    Where it doesn't

    • Usage-based costs vary by region and operation
    • Additional storage, ingestion, and extraction charges can apply
    • Cloud deployment limits it to web and API-based workflows

  • Apache Pinot — where it wins

    • Streams data from Kafka, Pulsar, and Kinesis in real time
    • Combines streaming and batch sources in one table
    • Supports SQL, REST access, joins, upserts, and varied indexes

    Where it doesn't

    • Self-hosted deployment requires infrastructure ownership
    • No managed cloud service is included
    • Its focus is real-time OLAP rather than general-purpose analytics

More comparisons

All OLAP software comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update