What Integrates with IBM Analytics for Apache Spark?

Find out what IBM Analytics for Apache Spark integrations exist in 2025. Learn what software and services currently integrate with IBM Analytics for Apache Spark, and sort them by reviews, cost, features, and more. Below is a list of products that IBM Analytics for Apache Spark currently integrates with:

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    RadiantOne Reviews
    Transform your existing infrastructure into an asset for the entire company with a platform that makes identity a business enabler. RadiantOne is a cornerstone for complex identity infrastructures. Using intelligent integration, you can improve your business outcomes, security and compliance posture, speed-to-market and more. RadiantOne allows companies to avoid custom coding, rework and ongoing maintenance in order to integrate new initiatives with existing environments. The deployment of expensive solutions is not on time or within budget, which negatively impacts ROI and causes employee frustration. Identity frameworks which cannot scale are a waste of time and resources. Employees struggle to provide new solutions for users. Rigid and static systems cannot meet changing requirements. This leads to duplication of efforts and repeated processes.
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    Switch Automation Reviews
    Switch Automation is a global real estate software company that helps property owners and facility managers reduce operating costs, improve energy efficiency and deliver exceptional occupant satisfaction. Our comprehensive smart building platform integrates with traditional building systems as well as Internet of Things (IoT) technologies to analyze, automate and control assets in real-time. We serve enterprise customers and partners in a variety of industries including financial services, retail, grocery, commercial real estate and more.
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    Apache Spark Reviews

    Apache Spark

    Apache Software Foundation

    Apache Sparkā„¢ serves as a comprehensive analytics platform designed for large-scale data processing. It delivers exceptional performance for both batch and streaming data by employing an advanced Directed Acyclic Graph (DAG) scheduler, a sophisticated query optimizer, and a robust execution engine. With over 80 high-level operators available, Spark simplifies the development of parallel applications. Additionally, it supports interactive use through various shells including Scala, Python, R, and SQL. Spark supports a rich ecosystem of libraries such as SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming, allowing for seamless integration within a single application. It is compatible with various environments, including Hadoop, Apache Mesos, Kubernetes, and standalone setups, as well as cloud deployments. Furthermore, Spark can connect to a multitude of data sources, enabling access to data stored in systems like HDFS, Alluxio, Apache Cassandra, Apache HBase, and Apache Hive, among many others. This versatility makes Spark an invaluable tool for organizations looking to harness the power of large-scale data analytics.
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