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Apache Pinot

Apache Pinot is a real-time distributed OLAP datastore, built to deliver scalable real-time analytics with low latency.

Apache Pinot

Apache Pinot Reviews and Details

This page is designed to help you find out whether Apache Pinot is good and if it is the right choice for you.

Features & Specs

  1. Real-time Analytics

    Apache Pinot is designed for real-time analytics on large-scale data. It is capable of ingesting data from streaming sources like Apache Kafka, providing low-latency query capabilities on freshly ingested data.

  2. High Throughput

    Pinot can handle high query loads and large datasets efficiently. Its architecture is optimized for distributed processing and fast query execution, making it suitable for use cases with high query throughput requirements.

  3. Columnar Storage

    Pinot utilizes a columnar storage format, which allows efficient compression and fast retrieval of highly selective query results, reducing I/O and improving query performance.

  4. Scalability

    Pinot is highly scalable and can be deployed across a distributed infrastructure. This makes it suitable for both growing startups and large enterprises with expanding data needs.

  5. Integration with Big Data Ecosystem

    Apache Pinot integrates seamlessly with other big data technologies like Apache Kafka, Hadoop, and Spark, making it easier for organizations to adopt it in existing tech stacks.

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Videos

How DoorDash and Uber use Apache Pinot ๐Ÿš— #podcast #shorts #uber #doordash #apachepinot #technology

Meetup: Apache Pinot Year in Review 2024

Running Realtime Analytics At Scale With Apache Pinot At Linkedin And Uber

Social recommendations and mentions

We have tracked the following product recommendations or mentions on various public social media platforms and blogs. They can help you see what people think about Apache Pinot and what they use it for.
  • Yet another end-to-end streaming dashboarding example
    In this post, we present an introductory example using Apache Pinot to ingest an Apache Kafka stream. This is an introductory post that builds upon existing Apache Pinot material from the official trainings and documentation. The purpose here is not just to rehash what is in the official docs, but a preparation for a second part. The idea, is to adapt the official examples to this end. Moreover, when I tried to... - Source: dev.to / 3 months ago

External sources with reviews and comparisons of Apache Pinot

Rockset, ClickHouse, Apache Druid, or Apache Pinot? Which is the best database for customer-facing analytics?
The biggest value behind Apache Pinot is that you can index each column, which allows it to process data at a super fast speed. โ€œItโ€™s like taking a pivot table and saving it to disk. So you can get this highly dimensional data with pre-computed aggregations and pull those out in what seems like supernaturally fast time,โ€ says Tim Berglund, Developer Relations at StarTree. (StarTree.ai offers Pinot as a service.)

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Is Apache Pinot good? This is an informative page that will help you find out. Moreover, you can review and discuss Apache Pinot here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.