Apache Pinot

Apache Pinot is a real-time distributed OLAP (Online Analytical Processing) datastore designed for ultra-low-latency analytics at high throughput. Originally developed at LinkedIn, it enables real-time ingestion and querying of data from both streaming and batch sources. Pinot is optimized for user-facing analytics applications, internal dashboards, anomaly detection, and ad hoc data exploration.

Features

• Real-time ingestion from streaming sources like Apache Kafka, Amazon Kinesis, and Apache Pulsar
• Batch ingestion from Hadoop, Amazon S3, Azure ADLS, and Google Cloud Storage
• Columnar storage format with smart indexing and pre-aggregation
• Ultra-low-latency query performance, even at petabyte scale
• Support for upserts and mutable data
• Rich indexing options including inverted, range, Bloom filter, StarTree, and geospatial
• SQL query interface with REST API and built-in query editor
• Built-in multitenancy for secure, isolated data access
• High concurrency support for hundreds of thousands of queries per second
• Integration with BI tools like Superset, Tableau, and Power BI

Capabilities

• Executes OLAP queries with sub-second latency
• Handles high-velocity, high-dimensional event data
• Supports both mutable and immutable data models
• Scales horizontally with no upper bound on data volume or query load
• Enables user-facing analytics with personalized, real-time insights
• Provides consistent performance based on cluster size and QPS thresholds
• Powers real-time dashboards and business metric monitoring
• Supports enterprise application development with aggregate views over microservices

Benefits

• Delivers fast, interactive analytics for end users and internal teams
• Reduces infrastructure costs with efficient storage and compute scaling
• Simplifies architecture by combining batch and streaming data in one system
• Enhances developer productivity with SQL-based access and flexible data modeling
• Improves decision-making with always-fresh, queryable data
• Enables seamless integration into modern data ecosystems and cloud platforms
• Backed by a strong open-source community and enterprise adoption