Best DevOps Software for Hadoop

Find and compare the best DevOps software for Hadoop in 2025

Use the comparison tool below to compare the top DevOps software for Hadoop on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    IBM DevOps Deploy Reviews
    IBM DevOps Deploy, previously known as IBM UrbanCode Deploy, is an application-release platform that enables the continuous delivery of applications to various environments by integrating deployment automation with extensive visibility, traceability, and auditing features. It enhances the frequency of software releases through automated and repeatable deployment processes that span development, testing, and production phases. The solution streamlines the deployment of multichannel applications, ensuring consistency and repeatability across both on-premises and cloud environments. By utilizing a centralized server, organizations can efficiently manage thousands of endpoints across multiple clouds, data centers, or mainframes. The platform increases robustness and simplifies the design of processes through established integrations with a wide array of tools and technologies, such as Jira, Jenkins, Kubernetes, Microsoft, ServiceNow, and WebSphere, ultimately fostering a more agile development environment. This comprehensive approach not only accelerates delivery but also enhances overall operational efficiency.
  • 2
    IBM StreamSets Reviews

    IBM StreamSets

    IBM

    $1000 per month
    IBM® StreamSets allows users to create and maintain smart streaming data pipelines using an intuitive graphical user interface. This facilitates seamless data integration in hybrid and multicloud environments. IBM StreamSets is used by leading global companies to support millions data pipelines, for modern analytics and intelligent applications. Reduce data staleness, and enable real-time information at scale. Handle millions of records across thousands of pipelines in seconds. Drag-and-drop processors that automatically detect and adapt to data drift will protect your data pipelines against unexpected changes and shifts. Create streaming pipelines for ingesting structured, semistructured, or unstructured data to deliver it to multiple destinations.
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