Pinot is a distributed OLAP database designed for real-time analytics at scale across multiple servers, while DuckDB is an embedded SQL database optimized for fast in-process analytics on single machines. Pinot excels with massive datasets and continuous ingestion; DuckDB prioritizes simplicity and query speed on local data.
Pinot vs DuckDB — Side-by-side comparison on A Versus B
Distributed real-time OLAP database for streaming analytics at scale
In-process columnar SQL database for analytical queries and OLAP workloads.
Architecture. Deployment Complexity. Real-time Ingestion Rate. Query Performance (1B rows). Scalability. Operational Overhead. Use Case Focus
Choose Pinot if you need to ingest millions of events per second continuously, serve sub-second queries on petabyte-scale data across distributed infrastructure, and run 24/7 production analytics systems (typical for LinkedIn-scale applications). Choose DuckDB if you prioritize simplicity, work with datasets under 1TB, need zero operational overhead, and want to embed SQL analytics directly into Python/R/Node.js applications or perform fast ad-hoc data exploration.
Apache Pinot: Full support (INNER, LEFT, RIGHT, FULL) | DuckDB: N/A
Apache Pinot: PQL + Limited SQL (60% compatibility) | DuckDB: N/A
Apache Pinot: 1000+ (LinkedIn, Uber, etc) | DuckDB: N/A
Apache Pinot: PQL (Custom) + Presto Bridge | DuckDB: N/A
Apache Pinot: Native tenant isolation | DuckDB: N/A
Apache Pinot: ~4-6x columnar | DuckDB: N/A
Apache Pinot: Ad-hoc queries (generalized) | DuckDB: N/A
Apache Pinot: Full support | DuckDB: N/A
Apache Pinot: 1,000,000-2,000,000 events/sec | DuckDB: N/A
Apache Pinot: 100-500ms (real-time) | DuckDB: N/A
Apache Pinot: 5-7 nodes (3 controllers + 2-4 brokers) | DuckDB: N/A
Apache Pinot: 100-500TB (hot data) | DuckDB: N/A
Apache Pinot: $0 (infrastructure only) | DuckDB: N/A
Apache Pinot: 0.1 (streaming) | DuckDB: N/A
Apache Pinot: Limited by infrastructure | DuckDB: 1 (single machine)
Apache Pinot: ~60% (Pinot Query Language variant) | DuckDB: 95% ANSI SQL
Apache Pinot: 500,000/sec (streaming) | DuckDB: N/A
Apache Pinot: 500+ (LinkedIn runs 1,000+) | DuckDB: N/A
Apache Pinot: Limited (phrase queries, basic tokenization) | DuckDB: N/A
Apache Pinot: 14-30 days (requires schema design and tuning) | DuckDB: N/A
Apache Pinot: Native SQL with PQL extensions (ANSI-compliant subset) | DuckDB: N/A
Apache Pinot: 16-32GB for analytics workload | DuckDB: N/A
Apache Pinot: Native (Kafka, S3, MQ) | DuckDB: Batch-focused only
Apache Pinot: 1000+ | DuckDB: Limited by single machine
Apache Pinot: Java, Python (limited) | DuckDB: Python, R, Java, C++, Node.js, Go
Annual Infrastructure Cost (1TB dataset)
Apache Pinot: 100-500 typical | DuckDB: N/A
Apache Pinot: Sub-second to 1 minute | DuckDB: N/A
Apache Pinot: Apache 2.0 Open Source | DuckDB: N/A
Apache Pinot: Kubernetes, on-premises, all cloud providers | DuckDB: N/A
Apache Pinot: 1M+ eps native | DuckDB: N/A
Apache Pinot: Self-hosted infrastructure costs (highly variable) | DuckDB: N/A
Apache Pinot: Free (self-hosted) / $0.10-0.50 (managed) | DuckDB: N/A
Apache Pinot: Manual cluster management required | DuckDB: N/A
Apache Pinot: 10+ billion events/day (proven at LinkedIn, Airbnb) | DuckDB: N/A
Apache Pinot: 40-80 hours (cluster provisioning, tuning) | DuckDB: N/A
Apache Pinot: 100-500 (depends on cluster config) | DuckDB: N/A
Apache Pinot: 50-200 | DuckDB: 2-64 (varies)
Apache Pinot: Yes (native distributed) | DuckDB: No (single-node only)
Apache Pinot: Yes | DuckDB: No (batch only)
Apache Pinot: N/A | DuckDB: 10-50 million rows/sec
Apache Pinot: N/A | DuckDB: 0.5-2 seconds
Apache Pinot: N/A | DuckDB: Partial (batch insert-optimized)
Apache Pinot: N/A | DuckDB: Single-threaded writes only
Apache Pinot: N/A | DuckDB: 5 years (since 2019)
Apache Pinot: N/A | DuckDB: Parquet, CSV, JSON, Iceberg, Hugging Face
Apache Pinot: N/A | DuckDB: Growing (100K+)
Query Latency (100M rows, simple aggregation)
Apache Pinot: N/A | DuckDB: Python, SQL, C++, R, Julia, Node.js
Apache Pinot: N/A | DuckDB: 2-5 (local install)
Apache Pinot: N/A | DuckDB: Not supported
Apache Pinot: N/A | DuckDB: No (single machine)
Apache Pinot: N/A | DuckDB: Fully supported
Apache Pinot: N/A | DuckDB: C++ (Rust bindings available)
Apache Pinot: N/A | DuckDB: 5 (Python, R, Java, Node.js, Go)
Apache Pinot: N/A | DuckDB: v0.10.0 (2024)
Total Cost of Ownership (Annual, 100TB dataset)
Query Latency (10GB dataset, simple aggregate)
Query Latency (1TB dataset, complex join)
Apache Pinot: N/A | DuckDB: 2 TB (local)
Apache Pinot: N/A | DuckDB: 1-5 simultaneous
Apache Pinot: N/A | DuckDB: No (requires external integration)
Query Performance on 10GB Parquet File (GROUP BY aggregation)
Apache Pinot: N/A | DuckDB: 2.1 GB (with compression)
Apache Pinot: N/A | DuckDB: 45ms (lightweight binary)
Number of Built-in Data Transformation Methods
Apache Pinot: N/A | DuckDB: 65 SQL functions + standard
Apache Pinot: N/A | DuckDB: 8.2K questions
Maximum Dataset Size (without disk streaming)
Apache Pinot: N/A | DuckDB: 1000+ GB (out-of-core)
Apache Pinot: N/A | DuckDB: Yes (ROW_NUMBER, LAG, LEAD, RANK, etc.)
Apache Pinot: N/A | DuckDB: Unlimited (disk-dependent)
Apache Pinot: N/A | DuckDB: None (local only)
Apache Pinot: N/A | DuckDB: 7 (Python, Node.js, Go, Rust, R, Java, C++)
Apache Pinot: N/A | DuckDB: No (embedded library)
Apache Pinot: N/A | DuckDB: 200 MB (compressed)
Apache Pinot: N/A | DuckDB: Full ANSI SQL + extensions
Apache Pinot: N/A | DuckDB: 7 (requires SQL)
How to choose between Apache Pinot and DuckDB
Pinot vs DuckDB — Structured Comparison Data
Machine-readable comparison dataset for Pinot vs DuckDB. Contains 137 attribute comparisons, 5 FAQ pairs.
Pinot vs DuckDB — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Pinot vs DuckDB.
Use Pinot when you need to ingest 1M+ events per second continuously, serve sub-second queries on datasets exceeding 1TB, or require 24/7 production analytics systems with high availability. Pinot's distributed architecture handles massive scale; DuckDB saturates at typical RAM limits (1-2TB). Companies like LinkedIn process 130+ trillion events daily with Pinot.
No. DuckDB replaces single-node analytical databases (SQLite, in-memory pandas) and excels for exploratory analytics, BI queries, and embedded analytics. Pinot replaces data warehouses and real-time analytics systems. DuckDB's 1TB limit and lack of real-time ingestion make it unsuitable for enterprise streaming use cases.
What are the total costs: Pinot vs DuckDB?
DuckDB: $0 (open-source, self-hosted). Pinot infrastructure: 4-node cluster costs ~$3,000-5,000/month (AWS m5.2xlarge × 4 + Zookeeper). However, Pinot's handling of massive scale eliminates need for secondary caching layers, and its real-time capability prevents costly ETL pipeline delays. ROI depends on query volume and data velocity.
DuckDB is not designed for continuous streaming. While you can periodically ingest new files (every 1-5 minutes), it lacks Pinot's streaming ingestion pipelines, watermarking, and late-arrival data handling. For true real-time analytics (sub-second latency with continuous updates), Pinot is required.
How does query performance compare on 100GB datasets?
On 100GB in-memory: DuckDB averages 0.5-1.5 seconds due to vectorized execution and aggressive optimization. Pinot: 1-3 seconds including distributed query coordination overhead. DuckDB wins on latency, but Pinot maintains consistency as data scales to 10TB+. At 1TB+, Pinot's distributed query execution becomes advantageous.
How to Choose Between Apache Pinot and DuckDB
Architecture: Apache Pinot has Distributed, multi-node cluster, while DuckDB has Embedded, single-process.
Deployment Complexity: Apache Pinot has Requires cluster setup, Zookeeper coordination, 6+ months typical implementation, while DuckDB has Single library import, 5-minute setup.
Real-time Ingestion Rate: Apache Pinot has 1M+ events/second at scale, while DuckDB has 10k-100k events/second (batch optimized).
Query Performance (1B rows): Apache Pinot has 2-5 seconds via distributed execution, while DuckDB has 0.5-2 seconds (in-memory, single machine).
Scalability: Apache Pinot has Horizontal: add nodes for 10TB+ datasets, while DuckDB has Vertical: limited by available RAM (typically 1TB).
Operational Overhead: Apache Pinot has High: cluster monitoring, rebalancing, failover management, while DuckDB has Minimal: no infrastructure dependencies.
Use Case Focus: Apache Pinot has 24/7 streaming analytics at enterprise scale, while DuckDB has Ad-hoc analytics, data science, ETL pipelines.