BigQuery is a fully managed cloud data warehouse optimized for batch analytics with SQL simplicity and no infrastructure management, while Apache Pinot is an open-source distributed OLAP database designed for real-time analytics on streaming data with sub-second query latencies. BigQuery suits enterprises wanting serverless analytics; Pinot suits companies needing millisecond-level real-time insights at scale.
BigQuery vs Apache Pinot — Side-by-side comparison on A Versus B
Fully managed cloud data warehouse for batch analytics with serverless scaling
Distributed real-time OLAP database for streaming analytics at scale
Query Latency. Data Model. Infrastructure Management. Cost Model. Pricing Predictability. Real-time Data Ingestion. SQL Compatibility
Choose BigQuery if you need a fully managed, zero-ops analytics solution with standard SQL, don't require sub-second latencies, and have budget for query-based pricing (ideal for enterprises with variable analytics workloads). Choose Apache Pinot if you need real-time analytics on streaming data with millisecond latencies, want cost predictability through self-hosting, and have engineering resources to manage infrastructure (ideal for ad-tech, fintech, and real-time dashboarding use cases).
Google BigQuery: 3-15 seconds | Apache Pinot: N/A
Google BigQuery: 0.25 hours | Apache Pinot: N/A
Google BigQuery: $6.25 per TB scanned | Apache Pinot: N/A
Google BigQuery: 12-15 (BigQuery ML preset models) | Apache Pinot: N/A
Google BigQuery: SQL, Python (BigQuery ML), JavaScript | Apache Pinot: N/A
Google BigQuery: Unlimited | Apache Pinot: N/A
Google BigQuery: 1 (Google Cloud only) | Apache Pinot: N/A
Google BigQuery: 5 formats (Parquet, ORC, Avro, CSV, JSON) | Apache Pinot: N/A
Google BigQuery: 1,000-10,000ms | Apache Pinot: 100-500ms
Google BigQuery: $6.25 | Apache Pinot: $0 (infrastructure only)
Google BigQuery: 15 (batch) | Apache Pinot: 0.1 (streaming)
Google BigQuery: 1 week | Apache Pinot: 12 weeks
Google BigQuery: Unlimited (serverless) | Apache Pinot: Limited by infrastructure
Google BigQuery: 10,000/sec (batch) | Apache Pinot: 500,000/sec (streaming)
Google BigQuery: $625,000 | Apache Pinot: $150,000
Google BigQuery: 100-500ms | Apache Pinot: 50-200ms
Google BigQuery: 5-15 seconds | Apache Pinot: N/A
Infrastructure Cost (Annual, 50TB dataset)
Google BigQuery: $18,750 ($6.25/TB × 50TB × 12 months) | Apache Pinot: N/A
Infrastructure Cost (Annual, 500TB dataset)
Google BigQuery: $187,500 ($6.25/TB × 500TB × 12 months) | Apache Pinot: N/A
Google BigQuery: 1-2 days (account + dataset creation) | Apache Pinot: N/A
Google BigQuery: 30% (requires Dataflow for preprocessing) | Apache Pinot: N/A
Google BigQuery: 2-4 hours (monitoring queries, access control) | Apache Pinot: N/A
Google BigQuery: Google Cloud region-level only | Apache Pinot: N/A
Google BigQuery: Unlimited (transparent to user) | Apache Pinot: N/A
Google BigQuery: 15-45 seconds | Apache Pinot: N/A
Google BigQuery: $25,000-$60,000 | Apache Pinot: N/A
Google BigQuery: 2-4 weeks | Apache Pinot: N/A
Google BigQuery: 2 (SQL, streaming via Pub/Sub) | Apache Pinot: N/A
Google BigQuery: Unlimited (cloud-based) | Apache Pinot: N/A
Google BigQuery: 10-15 minutes | Apache Pinot: N/A
Google BigQuery: Google Cloud only | Apache Pinot: N/A
Google BigQuery: 45 seconds | Apache Pinot: N/A
Google BigQuery: 5,000-30,000 ms | Apache Pinot: <100 ms
Google BigQuery: 60-120 seconds | Apache Pinot: <1 second
Google BigQuery: $6.25 | Apache Pinot: Free (self-hosted) / $0.10-0.50 (managed)
Google BigQuery: Fully serverless | Apache Pinot: Manual cluster management required
Google BigQuery: ~95% ANSI SQL compatible | Apache Pinot: ~60% (Pinot Query Language variant)
Google BigQuery: Petabyte-scale (pricing limited) | Apache Pinot: 10+ billion events/day (proven at LinkedIn, Airbnb)
Google BigQuery: 1-2 hours (sign-up to first query) | Apache Pinot: 40-80 hours (cluster provisioning, tuning)
Google BigQuery: 1000+ (automatic) | Apache Pinot: 100-500 (depends on cluster config)
Google BigQuery: N/A | Apache Pinot: 50-100ms
Google BigQuery: N/A | Apache Pinot: 150-300MB
Google BigQuery: N/A | Apache Pinot: 10K-50K
Google BigQuery: N/A | Apache Pinot: $1500-3000
Google BigQuery: N/A | Apache Pinot: Full support (INNER, LEFT, RIGHT, FULL)
Google BigQuery: N/A | Apache Pinot: PQL + Limited SQL (60% compatibility)
Google BigQuery: N/A | Apache Pinot: 1000+ (LinkedIn, Uber, etc)
Google BigQuery: N/A | Apache Pinot: 8-10x
Google BigQuery: N/A | Apache Pinot: 100-500ms
Google BigQuery: N/A | Apache Pinot: 100-400MB
Google BigQuery: N/A | Apache Pinot: PQL (Custom) + Presto Bridge
Google BigQuery: N/A | Apache Pinot: Native tenant isolation
Google BigQuery: N/A | Apache Pinot: $2000-3500
Google BigQuery: N/A | Apache Pinot: 500-2000ms
Google BigQuery: N/A | Apache Pinot: 500,000-1,000,000
Google BigQuery: N/A | Apache Pinot: 5-8 nodes
Google BigQuery: N/A | Apache Pinot: 5,300
Google BigQuery: N/A | Apache Pinot: ~4-6x columnar
Google BigQuery: N/A | Apache Pinot: Ad-hoc queries (generalized)
Google BigQuery: N/A | Apache Pinot: Full support
Google BigQuery: N/A | Apache Pinot: 1,000,000-2,000,000 events/sec
Google BigQuery: N/A | Apache Pinot: 100-500ms (real-time)
Google BigQuery: N/A | Apache Pinot: $200-400
Google BigQuery: N/A | Apache Pinot: 16-64GB
Google BigQuery: N/A | Apache Pinot: 5-7 nodes (3 controllers + 2-4 brokers)
Google BigQuery: N/A | Apache Pinot: 100-500TB (hot data)
Google BigQuery: N/A | Apache Pinot: 500+ (LinkedIn runs 1,000+)
Google BigQuery: N/A | Apache Pinot: Limited (phrase queries, basic tokenization)
Google BigQuery: N/A | Apache Pinot: 14-30 days (requires schema design and tuning)
Google BigQuery: N/A | Apache Pinot: Native SQL with PQL extensions (ANSI-compliant subset)
Google BigQuery: N/A | Apache Pinot: 16-32GB for analytics workload
Google BigQuery: N/A | Apache Pinot: 8,200+
Google BigQuery: N/A | Apache Pinot: 240-480
Google BigQuery: N/A | Apache Pinot: 100-500
Google BigQuery: N/A | Apache Pinot: 100,000+
Google BigQuery: N/A | Apache Pinot: 5-7
Google BigQuery: N/A | Apache Pinot: Native (Kafka, S3, MQ)
Google BigQuery: N/A | Apache Pinot: 1000+
Google BigQuery: N/A | Apache Pinot: Java, Python (limited)
Annual Infrastructure Cost (1TB dataset)
Google BigQuery: N/A | Apache Pinot: 50,000-150,000
Google BigQuery: N/A | Apache Pinot: 100-500 typical
Google BigQuery: N/A | Apache Pinot: <100ms
Google BigQuery: N/A | Apache Pinot: Sub-second to 1 minute
Google BigQuery: N/A | Apache Pinot: Apache 2.0 Open Source
Google BigQuery: N/A | Apache Pinot: Kubernetes, on-premises, all cloud providers
Google BigQuery: N/A | Apache Pinot: 1M+ eps native
Google BigQuery: N/A | Apache Pinot: Self-hosted infrastructure costs (highly variable)
Google BigQuery: N/A | Apache Pinot: 1,000,000+
Google BigQuery: N/A | Apache Pinot: 2-5
Google BigQuery: N/A | Apache Pinot: 10,000+
Google BigQuery: N/A | Apache Pinot: 3-5
Google BigQuery: N/A | Apache Pinot: 50-200
Google BigQuery: N/A | Apache Pinot: Yes (native distributed)
Google BigQuery: N/A | Apache Pinot: Yes
How to choose between Google BigQuery and Apache Pinot
BigQuery vs Apache Pinot — Structured Comparison Data
Machine-readable comparison dataset for BigQuery vs Apache Pinot. Contains 95 attribute comparisons, 5 FAQ pairs.
BigQuery vs Apache Pinot — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for BigQuery vs Apache Pinot.
When should I choose BigQuery over Pinot?
Choose BigQuery if you need a fully managed analytics platform with zero ops overhead, use standard SQL for complex analytical queries, don't require real-time (sub-second) query latencies, and prefer paying per query with automatic scaling. BigQuery is ideal for data warehousing, business intelligence, and historical analysis where latency tolerance is 5-30 seconds.
When should I choose Pinot over BigQuery?
Choose Pinot if you need real-time analytics with sub-100ms query latencies for live dashboards, have streaming data (Kafka, S3) requiring ingestion latency <1 second, want predictable costs through self-hosting, and have engineering resources for cluster operations. Pinot excels in ad-tech analytics, real-time metrics, and gaming leaderboards.
How does cost scale comparing a 10TB monthly query scan?
BigQuery (on-demand): 10TB × $6.25/TB = $62.50/month minimum. Pinot (self-hosted): Infrastructure costs ~$500-2000/month for a 3-node cluster, but zero per-query charges regardless of data scanned. BigQuery is cheaper for small query volumes; Pinot becomes cost-effective at high query volumes (1000+ queries/day).
Can I migrate from BigQuery to Pinot or vice versa?
Migration is possible but non-trivial. Queries require rewriting since BigQuery uses standard SQL while Pinot uses PQL (Pinot Query Language). Data schemas differ: BigQuery is columnar-optimized for analytical queries; Pinot is optimized for real-time indexing. Most organizations run both in parallel rather than migrating entirely.
What's the learning curve for each platform?
BigQuery: Minimal — standard SQL knowledge transfers directly; UI is intuitive; most teams productive in days. Pinot: Steep — requires understanding distributed systems, cluster configuration, Kafka/streaming ingestion, query optimization for real-time workloads; typical ramp-up is 2-4 weeks for engineers.
How to Choose Between Google BigQuery and Apache Pinot
Query Latency: Google BigQuery has 5-30 seconds (typical), while Apache Pinot has <100ms (typical).
Data Model: Google BigQuery has Batch-oriented columnar storage, while Apache Pinot has Real-time streaming + batch hybrid.
Infrastructure Management: Google BigQuery has Fully serverless, zero ops, while Apache Pinot has Requires cluster management and ops.
Cost Model: Google BigQuery has $6.25 per TB scanned (on-demand), while Apache Pinot has Self-hosted (free) or managed (variable).
Pricing Predictability: Google BigQuery has Pay-per-query, scales with data scanned, while Apache Pinot has Predictable infrastructure costs (self-hosted).
Real-time Data Ingestion: Google BigQuery has Streaming Insert API (~1-2 min latency), while Apache Pinot has Native streaming ingestion (<1 second).
SQL Compatibility: Google BigQuery has Standard SQL + BigQuery extensions, while Apache Pinot has SQL-like query language (PQL).