Five Ways Automation Speeds Up Big Data Deployments

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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.

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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