Compare the Top Real-Time Data Streaming Tools that integrate with Hadoop as of August 2025

This a list of Real-Time Data Streaming tools that integrate with Hadoop. Use the filters on the left to add additional filters for products that have integrations with Hadoop. View the products that work with Hadoop in the table below.

What are Real-Time Data Streaming Tools for Hadoop?

Real-time data streaming tools enable organizations, big data and machine learning professionals, and data scientists to stream data in real time, and build data models when new data is created or ingested. Compare and read user reviews of the best Real-Time Data Streaming tools for Hadoop currently available using the table below. This list is updated regularly.

  • 1
    StarTree

    StarTree

    StarTree

    StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. • Gain critical real-time insights to run your business • Seamlessly integrate data streaming and batch data • High performance in throughput and low-latency at petabyte scale • Fully-managed cloud service • Tiered storage to optimize cloud performance & spend • Fully-secure & enterprise-ready
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  • 2
    Apache Flink

    Apache Flink

    Apache Software Foundation

    Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. Any kind of data is produced as a stream of events. Credit card transactions, sensor measurements, machine logs, or user interactions on a website or mobile application, all of these data are generated as a stream. Apache Flink excels at processing unbounded and bounded data sets. Precise control of time and state enable Flink’s runtime to run any kind of application on unbounded streams. Bounded streams are internally processed by algorithms and data structures that are specifically designed for fixed sized data sets, yielding excellent performance. Flink is designed to work well each of the previously listed resource managers.
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