BigQuery is a fully managed cloud data warehouse optimized for analytical queries with built-in ML capabilities, while Apache Pinot is an open-source distributed OLAP database designed for real-time analytics on streaming data. BigQuery excels at batch processing large datasets, whereas Pinot specializes in sub-second query latency on continuously ingested data.
Google BigQuery vs Apache Pinot — Side-by-side comparison on A Versus B
Fully managed cloud data warehouse for analytics with integrated ML capabilities
Real-time OLAP engine optimized for low-latency dimensional analytics on streaming data.
Query Latency. Deployment Model. Data Ingestion Rate. Machine Learning Integration. Cost Structure. Query Complexity Support. Operational Overhead
Choose BigQuery if you need a fully managed, scalable data warehouse for complex analytical workloads with ML capabilities and can tolerate 1-10 second query latencies. Choose Pinot if you require sub-second real-time analytics on streaming data, have DevOps resources available, and need to minimize query-based costs.
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: Exabyte+ | 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: 15-60 seconds (batch/streaming) | Apache Pinot: 0.1 (streaming)
Google BigQuery: 1 week | Apache Pinot: 12 weeks
Google BigQuery: Unlimited (serverless) | Apache Pinot: 1000+
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: 500-2000ms
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: 0.5-1 week | 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 managed (zero DevOps) | Apache Pinot: Self-hosted (high DevOps)
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: 1,000-10,000ms | Apache Pinot: N/A
Google BigQuery: $0.02 (long-term storage) | Apache Pinot: N/A
Google BigQuery: $6.25 | Apache Pinot: N/A
Google BigQuery: 99% SQL standard | Apache Pinot: 85% SQL coverage
Google BigQuery: Google Cloud only | Apache Pinot: On-prem, Kubernetes, multi-cloud
Google BigQuery: Minimal (fully managed, autoscaling) | Apache Pinot: N/A
Google BigQuery: 10-30 seconds | Apache Pinot: N/A
Google BigQuery: $50,000-$75,000 | Apache Pinot: N/A
Google BigQuery: 0-1 (analyst can self-serve) | Apache Pinot: N/A
Google BigQuery: Unlimited (auto-scaling) | Apache Pinot: N/A
Google BigQuery: $7 (BigQuery native) | Apache Pinot: N/A
Google BigQuery: 0% (cloud-native, N/A) | Apache Pinot: N/A
Google BigQuery: 2 (UDFs and built-in functions only) | Apache Pinot: N/A
Query Latency (Typical Analytical Query)
Google BigQuery: 1-10 seconds | Apache Pinot: <1 second
Google BigQuery: 100,000 rows/sec | Apache Pinot: 1,000,000+ rows/sec
Google BigQuery: $6.25 | Apache Pinot: $0 (open-source)
Google BigQuery: 15+ models (BQML) | Apache Pinot: 0 (requires external tools)
Google BigQuery: Petabyte-scale (unlimited) | Apache Pinot: Petabyte-scale (distributed)
P99 Query Latency (1B rows, aggregation)
Google BigQuery: N/A | Apache Pinot: 100-500ms
Ingestion Latency (Kafka to query-ready)
Google BigQuery: N/A | Apache Pinot: 0.5-2 seconds
Google BigQuery: N/A | Apache Pinot: Standard ANSI SQL
Google BigQuery: N/A | Apache Pinot: 0.5-2TB per node
Google BigQuery: N/A | Apache Pinot: Kafka, Pulsar, Kinesis, S3
Google BigQuery: N/A | Apache Pinot: 15,000+
Google BigQuery: N/A | Apache Pinot: StarTree (24/7 SLA options)
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: 3:1 to 10:1 (avg 6:1)
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: 15:1 to 25:1
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: 50-200ms
Google BigQuery: N/A | Apache Pinot: $120-180
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
Typical Query Latency (1B rows, GROUP BY)
Google BigQuery: N/A | Apache Pinot: 50-500ms
Google BigQuery: N/A | Apache Pinot: 0.1-0.3x
Google BigQuery: N/A | Apache Pinot: Not optimized (secondary feature)
Google BigQuery: N/A | Apache Pinot: 9,200+
Google BigQuery: N/A | Apache Pinot: Native (Kafka, S3, HDFS)
Google BigQuery: N/A | Apache Pinot: Medium-High (columnar tuning)
Google BigQuery: N/A | Apache Pinot: 10+ PB (proven at scale)
Google BigQuery: N/A | Apache Pinot: Native PinotSQL (full ANSI compliance)
Google BigQuery: N/A | Apache Pinot: 70%
Google BigQuery: N/A | Apache Pinot: 5-7 components
Google BigQuery: N/A | Apache Pinot: 45-60 minutes
Google BigQuery: N/A | Apache Pinot: 500K
Google BigQuery: N/A | Apache Pinot: 100K-500K
Google BigQuery: N/A | Apache Pinot: 8+ (Broker, Server, Controller, Minion)
Google BigQuery: N/A | Apache Pinot: 100M+ with dictionary encoding
Google BigQuery: N/A | Apache Pinot: 1-5GB segments
Google BigQuery: N/A | Apache Pinot: 1-2 months
How to choose between Google BigQuery and Apache Pinot
Google BigQuery vs Apache Pinot — Structured Comparison Data
Machine-readable comparison dataset for Google BigQuery vs Apache Pinot. Contains 137 attribute comparisons, 5 FAQ pairs.
Google BigQuery vs Apache Pinot — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Google BigQuery vs Apache Pinot.
Use BigQuery for batch analytics, complex queries, and ML workloads on GCP. Use Pinot when you need real-time dashboards with sub-second latency, high-volume streaming ingestion (>100K rows/sec), and cost control. BigQuery excels at "what happened," Pinot excels at "what's happening now."
What's the cost difference between BigQuery and Pinot?
BigQuery charges $6.25 per TB of data scanned; a 10TB analytical query costs $62.50. Pinot is free (open-source), but you pay for infrastructure (Kubernetes cluster, compute nodes). For 1M rows/sec streaming with Pinot, expect ~$5K-$15K/month in cloud infrastructure. BigQuery is cheaper for ad-hoc queries; Pinot is cheaper for continuous high-volume ingestion.
Can BigQuery do real-time analytics like Pinot?
BigQuery's streaming inserts (100K rows/sec) are 10x slower than Pinot's (1M+ rows/sec). More critically, BigQuery's query latency is 1-10 seconds, unsuitable for real-time dashboards requiring <500ms responses. Pinot is purpose-built for sub-second analytics on streaming data. For real-time use cases, Pinot is the better choice.
Does Pinot have machine learning capabilities like BigQuery ML?
No. Pinot has no built-in ML; you must use external tools (Python, scikit-learn, TensorFlow). BigQuery ML (BQML) offers 15+ pre-built models for regression, classification, clustering, and time series forecasting via SQL. If ML is critical, BigQuery is superior.
Which database requires more operational overhead?
BigQuery is fully managed by Google; you focus only on queries and data loading. Pinot requires significant DevOps: provisioning clusters, managing replication, scaling nodes, monitoring health, patching software. If your team lacks DevOps expertise, BigQuery's simplicity is a major advantage.
How to Choose Between Google BigQuery and Apache Pinot
Query Latency: Google BigQuery has 1-10 seconds (typical analytical query), while Apache Pinot has <1 second (real-time optimized).
Deployment Model: Google BigQuery has Fully managed SaaS (Google Cloud), while Apache Pinot has Self-hosted open-source.
Data Ingestion Rate: Google BigQuery has 100K rows/sec (streaming insert), while Apache Pinot has 1M+ rows/sec (streaming).
Machine Learning Integration: Google BigQuery has Native BigQuery ML (BQML) with 15+ models, while Apache Pinot has No built-in ML; requires external tools.
Cost Structure: Google BigQuery has $6.25 per TB scanned (standard pricing), while Apache Pinot has Free open-source; infrastructure costs only.
Query Complexity Support: Google BigQuery has Complex joins, window functions, 99% SQL standard, while Apache Pinot has Simple to moderate queries, 85% SQL coverage.
Operational Overhead: Google BigQuery has Minimal (fully managed), while Apache Pinot has High (requires DevOps expertise).