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    Five Ways Automation Speeds Up Big Data Deployments

    Hadoop has come a long way since its inception. From its early days as a platform, to an index for the Web, it has evolved to its current interactive, real-time and batch processing capabilities spanning gigabytes to petabytes of content. However, as enterprises move Hadoop from pilot to production environments, they are finding the “on-boarding” process slow and complicated.

    Along with Hadoop, other Big Data technologies are complex and challenging to set up, sometimes generating large costs for support and maintenance. This is not a scalable model for customers who want to efficiently move Hadoop into production networks. That said, here’s a breakdown of the five ways automation speeds up the Big Data on-boarding process from Big Data management and security vendor Zettaset.

    Five Ways Automation Speeds Up Big Data Deployments - slide 1

    Automating Big Data

    Click through for five ways automation helps speed up and ease the complexity of Big Data deployments, as identified by Zettaset.

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    Hadoop Is Challenging

    Most enterprises are at a stage where the Big Data repository is already there, but how the enterprise can effectively manage and leverage that data is where Big Data technologies, like Hadoop, come in. However, Hadoop deployment is complex and still largely a time- and resource-intensive manual process, sometimes resulting in significant costs for support and maintenance. This is neither scalable nor efficient when it’s time to move Hadoop into production networks, and has been a factor in slowing overall Hadoop adoption.

    Five Ways Automation Speeds Up Big Data Deployments - slide 3

    Operation Efficiencies

    Although wary to admit it, branded open-source distributions rely heavily on manual processes for cluster deployment and ongoing configuration, and lack the process automation capabilities typically found in more mature database technologies. Automating multiple functions, like provisioning, installation, configuration and testing of the software, within Hadoop improves operational efficiencies and eliminates the burden from IT, who can then move specialized resources away from database maintenance and onto more strategic activities such as application deployment.

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    Operational Costs

    Users expecting lower operational costs by using Hadoop software and infrastructure are surprised to find they must spend enormous sums for software support and maintenance in the form of recurring “subscription” fees. Automation significantly offsets IT resource requirements, support and maintenance costs associated with Hadoop deployment. And as the size of the Hadoop cluster grows, so do the savings associated with automation.

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    Control and Security

    Users can feel trapped by a distribution vendor’s lock on their Hadoop environments. Isn’t one of the goals of open source software to eliminate vendor lock and provide IT with greater flexibility? Regaining control of your Hadoop cluster environment through automation makes IT more self-supporting. Adding comprehensive security by automatically extending encryption, access control and security policy enforcement across the Hadoop environment addresses enterprise requirements for IT compliance and governance.

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    Scalability for the Enterprise

    A mix of process automation, resultant cost and resource savings streamlines scalability for enterprises that are migrating Hadoop from pilot to production. Consider a situation involving the expansion of a Hadoop cluster from 10 to 50 nodes…or how about 50 to 250 nodes? Simplifying Hadoop deployment for the enterprise with software automation eliminates many of the daunting manual configuration processes that create excessive IT overhead and make cluster expansion a real headache. Support costs and IT resource requirements can now be prudently controlled, and migration plans can be executed on a realistic schedule that meets the needs of stakeholders within the enterprise.

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