UPDATED 17:29 EDT / NOVEMBER 20 2013

Treasure Data leads public cloud big data services market — for now

Treasure Data has taken a leadership position in providing the first end-to-end public cloud-based big data analysis service, writes Jeff Kelly in “Treasure Data Looks to the Cloud to Unlock Big Data Value”. The just announced service provides a complete workflow from data ingestion through transformation & storage, to processing, analytics and visualization.

The real value in Treasure Data, Kelly writes, is in the end-to-end cloud service, developed by company founders Hiro Yoshikawa and Kaz Ohta, which shields users from the complexities of big data capture and analysis. For data ingestion it has its own Treasure Agent that it deploys on customers’ servers. This taps into internal data streams, performs some minimal transformations, compresses & uploads the data to Treasure Data’s storage and processing platform, which runs on AWS. While it uses MapReduce for processing data, it has its own proprietary columnar database, Plazma, and does not use Hadoop. For visualization it offers several options including Tableau and its own data visualization tool.s in its en

Its timing is spot on. Increasing numbers of companies are moving beyond big data proof-of-concept trials, and the literature is full of stories about companies realizing major advantage from predictive analytics based on big data. But companies are struggling to scale up Hadoop proof-of-concept deployments to production-level sizes with tens to hundreds of nodes while maintaining performance levels.

And providing big data analysis as a service makes sense for a couple of reasons. First, often the vast majority of the data comes from external, cloud sources such as major social media sites. It is much faster and easier to move the processing to these often very large data streams than to try to import them to an internal system across the Internet. Also often public cloud services have higher security levels than private data centers. As a result of these realities, the resistance to moving internal data to public cloud systems to combine it with big data from public sources is breaking down.

However, Kelly says, while Treasure Data leads today in providing a unified service, it faces large-scale competitors including AWS itself. Amazon has accumulated most of the pieces of an end-to-end service, and it is only a matter of time before it integrates those pieces.

To keep ahead, Treasure Data will need to continue to innovate and add services, starting with push-button access to third-party data streams and compatibility with more analysis and visualization tools and addressing the security and privacy concerns of enterprise CIOs. Kelly recommends that enterprise big data practitioners struggling to scale Hadoop proof-of-concept deployments to production levels consider public cloud services like Treasure Data. He also warns that big data analysis is only one part of the larger business transformation life cycle. Beyond large volumes of historic data, enterprises must also leverage streaming analytics and machine learning to automate business processes and architect systems to deliver the right analysis to the right place at the right time.

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