Apache™ Hadoop® Enables Large Scale, Distributed Storage and Processing on Commodity Hardware

Apache Hadoop is a software package that includes a wide range of data processing engines on top of a distributed file system. It was designed to run a variety of computations, especially analytical jobs, on extremely large volumes of data in parallel across many commodity servers in a cluster. Example use cases on Hadoop include data lakes, customer 360 degree views, recommendations, security analytics, and clickstream analysis.

Key Features

  • Converged Data Platform: You typically need to run Hadoop alongside other modern technologies like NoSQL databases and event streaming systems. With MapR, convergence gives you real-time data access no matter how the data is delivered and stored.
  • Enterprise-Grade Capabilities: In your business-critical big data environments, reliability, security, multi-tenancy, and speed are all important components. MapR customers achieve 99.999% uptime and no data loss on a broad collection of distinct data sets, all while getting more output from fewer resources to lower their total cost of ownership (TCO).
  • Open Choice, Open Source: When choosing a Hadoop vendor, you want options. You want to choose the right tool for the job, as well as the right time to upgrade. MapR gives you multiple choices for key capabilities, as well as support for multiple project versions.
    • Project choices. Get support on a broad set of Hadoop projects, including the entire Apache Spark™ stack, Apache Drill, Impala, Hive, etc.
    • Monthly certified updates. Get access to the latest cutting-edge projects on Hadoop.
    • Backward compatibility. Upgrade your applications on your own schedule since platform upgrades will support prior project versions.

Use Cases

  • How Cisco IT Built Big Data Platform Using MapR: Cisco IT built a Big Data Platform to transform data management and provide big data analytics services to Cisco business teams. CIsco used MapR for their enterprise Hadoop architecture to unlock hidden business intelligence of their globally distributed large data sets, while also providing service-level agreements (SLAs) for internal customers.
    Case study
  • comScore Reliably Processes Over 1.7 Trillion Events Every Month on MapR: By using MapR, comScore is able to easily manage and significantly scale their Hadoop cluster, create more files faster, process more data faster, and produce better streaming and random I/O results than other Hadoop distributions – and do so confidently knowing they can count on data protection and disaster recovery functions when needed.
    Case study
  • Quantium Captures New Niche in Data Analytics Market: Australian consumers are among the most technologically sophisticated in the world, using a wide range of applications and devices to shop whenever and wherever they choose. They demand to be served quickly, which creates opportunities for highly responsive companies. Above all, consumers value a personalized experience, one with messages, recommendations, and promotions tailored to each individual.
    Case study
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