Five Ways Automation Speeds Up Big Data Deployments

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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.

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.


Related Topics : Vulnerabilities and Patches, Resellers, Broadcom, Broadband Services, Supercomputing

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