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Hadoop

Open-source software for reliable, scalable, distributed computing

Hadoop Alternatives

The best Hadoop alternatives based on verified products, votes, reviews and other factors.
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  1. Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

  2. Hadoop-Related

  3. Seamless project management and collaboration for your team.

    $12.0 / Monthly (Per user)

  4. Analytics Engine is a combined Apache Spark and Apache Hadoop service for creating analytics applications.

  5. MapR is a leading high-performance data management or IT management solution that integrates Apache Drill, Hadoop and Spark with real-time global event streaming, scalable enterprise storage, and database capabilities in order to control large appli…

  6. MongoDB (from "humongous") is a scalable, high-performance, open source NoSQL database.

    open-source

  7. Datameer is a business-user-focused business intelligence (BI) platform for Hadoop.

  8. The world's most popular open source database

  9. The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.

  10. PostgreSQL is a powerful, open source object-relational database system.

  11. Social media, analytics, marketing, sales, support, infrastructure... all in one place. Track your entire business in real-time.

  12. The BI & Dashboard Software to handle multiple, large data sets.

  13. Tableau Online Secure Login Page. Sign in to Tableau Online

Hadoop Reviews

External sources with reviews and comparisons of Hadoop

5 Best-Performing Tools that Build Real-Time Data Pipeline
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than relying on hardware to...
Read all Hadoop reviews →

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This article was published on | Author: | Publisher: SaaSHub
Categories: Databases, Big Data, Relational Databases