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Cloud computing and big data analytics are a match made in heaven. I’ve explained why before, but essentially it’s because the cloud model lets users leverage a service provider’s infrastructure investment and subject-matter expertise without having to build them in-house. Done right, big data in the cloud is like a marriage of managed services and Software-as-a-Service, only using very powerful software.

Thankfully, big data and the cloud have already found each other. Although it’s still very early in the evolution of this combination — experts predict major investment in this area going forward — several companies have already melded the two into a variety of unique services.

Quantivo. Quantivo just announced its foray into the space Tuesday with a cloud-based version of its analytics platform. The platform combines business data from multiple sources, transforms and enriches it, then lets customers work with it via Quantivo’s specialized interface. Because knowing the right questions to ask is often one of the more difficult aspects of big data, Quantivo says its technology also takes some of the guesswork out of the process by “intelligently auto-compiling lists of patterns” in customers’ datasets.

1010data. 1010data actually has been doing big data as a service for more than a decade, before anyone was talking about the cloud. It provides a variety of services for specific big data use cases, including data warehousing, business intelligence advanced analytics. Although 1010data utilizes Hadoop and other traditional big data tools to power its service, its Customers interact with the service using familiar tools such as spreadsheets that make it easier to find the connections and trends they’re looking for.





Opera Solutions. Opera Solutions is an interesting company, because although it’s doing $100 million in revenue a year, relatively few people have heard of it. But its service is pretty compelling: Customers upload their data to Opera’s platform, which then analyzes it and delivers results based on the relevant “signals” in a customer’s data set. Not content with providing generic analysis to customers, Opera focuses on each customer’s specific needs and employs experts in a variety of industries to help it cater unique analytics programs for each customer.



IBM. IBM (s ibm) has seemingly limitless options in terms of providing big data analytics as a cloud-based service, but its current strategy appears centered around Hadoop. When IBM launched its SmartCloud cloud computing platform in April, it promised that Hadoop workloads will be part of it. A likely candidate to provide that capability is InfoSphere BigInsights, IBM’s Hadoop-based software for analyzing and visualizing large quantities of unstructured data. BigInsights previously was available as a service on IBM’s test-and-development cloud that SmartCloud replaced.





Amazon Web Services(s amzn). AWS isn’t providing actual analytics as a service, just the parallel processing framework and computing power necessary to do them at scale. Its Elastic MapReduce platform is a cloud-based Hadoop implementation onto which users port their Hadoop applications, then upload their data and run the workload. Like all things AWS, customers only pay for the resources used while the job’s running, as well as for storing the data in AWS’s S3 storage service.

HPCC Systems. LexisNexis spinoff and Hadoop alternative HPCC Systems plans to give customers cloud-based access to a system running the company’s HPCC data-processing software. During an interview during Structure 2011, CTO Armando Escalante noted the company might even offer up its own massive data sets — which span the financial, legal and intelligence sectors, among others — to be processed by customers’ applications.