Six Details Your Big Data Provider Won't Tell You

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Scaling Up or Down Is Onerous

Organizations try to design their Big Data architectures with scalability in mind. Yet, they often underestimate how much storage and compute power they'll actually need and how quickly their data will grow. Scaling requires adding more hardware and tweaking software. It can take weeks to do when you really want to increase capacity now. In contrast, an organization that over predicts its workload could be stuck with significant sunk costs for underutilized hardware and support.

In a report released in January, Capgemini reported that, while 60 percent of executives believed Big Data will disrupt their industry within the next three years, only 13 percent of organizations in the survey have achieved full-scale production of their Big Data implementations. Moreover, the research found that only 27 percent of respondents described their Big Data initiatives as successful.

This is not a surprise to Ashish Thusoo, CEO and founder of the Big Data-as-a-service company Qubole. As one of the original creators of Facebook's data infrastructure and authors of Apache Hive, he's intimately familiar with the challenges of working with Hadoop, Spark, Hive, Presto and other related technologies. Building out an on-premises Big Data infrastructure takes significant time, investment and technical expertise and brings with it a lot of risk to boot. That's why Thusoo believes Big Data belongs in the cloud.

For organizations currently considering their own Big Data deployments, here are six things Big Data software providers won't tell you about on-premises solutions.

 

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

 
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