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Apache Pinot is a distributed, open-source OLAP datastore designed to provide real-time analytics with low latency and high throughput.
Apache Pinot is a real-time distributed OLAP (Online Analytical Processing) datastore developed to power user-facing analytical applications. It excels at providing immediate insights from high-velocity, high-volume data streams.
Traditional databases often struggle to provide real-time, low-latency analytical queries on rapidly changing, large-scale datasets. Pinot is built specifically to address this challenge for Online Analytical Processing (OLAP) workloads.
Achieve query latency in milliseconds, even on datasets spanning terabytes.
Handle millions of events per second from streaming sources like Kafka or Kinesis.
Utilizes columnar storage, various indexes (sorted, inverted, star-tree), and intelligent pruning techniques for query performance.
Apache Pinot is designed for scenarios where real-time analytical queries on large datasets are critical. Common use cases include:
Powering user-facing dashboards within applications (e.g., LinkedIn's Who's Viewed Your Profile, Uber's real-time monitoring).
Provides instant insights directly within the application, enhancing user experience and engagement.
Real-time monitoring of business metrics, application performance, network traffic, or IoT data streams.
Enables immediate detection of issues or trends, allowing for quicker response times.
Analyzing user behavior clicks, impressions, and activity streams in real-time for personalization or immediate segmentation.
Allows for instant understanding of user interactions to drive personalization or targeted actions.
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