NetApp is a fast growing provider of storage technology. Its devices
“phone home” regularly, sending unstructured auto-support log and
configuration data back to centralized data centers. This data is used
to provide timely support, to improve sales, and to plan product
improvements. To allow this, data is collected, organized, and
analyzed. The system currently ingests 5 TB of compressed data per
week, which is growing 40% per year. NetApp was previously storing
flat files on disk volumes and keeping summary data in relational
databases. Today, NetApp is working with Accenture to design, build,
and implement the enterprise transformation project for next
generation auto-support, with Think Big Analytics as a partner and
expert in Big Data solutions. The new system uses Hadoop, HBase and
related technologies to ingest, organize, transform and present
auto-support data. This will enable business users to make decisions
and provide timely response, and will enable automated response based
on predictive models. Key requirements include:
In this session we look at the lessons learned while designing and
implementing a system to:
Ron founded Think Big Analytics to help customers leverage new data processing technologies like Hadoop and NoSQL databases and R for statistical analysis. Works with customers to identify opportunities and rapidly develop solutions that integrate data and extract information.
Previously Ron was the VP of Engineering for Quantcast. Each day Quantcast ingests 10 billion events and processes two petabytes of data using Hadoop. The Quantcast map reduce stack handles production data processing, ad hoc analysis, data mining and machine learning. Prior to that Ron was a founder of enterprise consulting companies C-bridge and New Aspects.
Kumar Palaniappan is an Enterprise Architect at NetApp where he leads efforts at adopting Hadoop technologies for strategic applications. Previously Kumar was an architect at Cisco Systems, responsible for large scale, mission critical architectures.
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