Jodok Batlogg

Super Simple Real-Time Big Data Backend: Crate Data

Date: This event took place live on July 08 2014

Presented by: Jodok Batlogg

Duration: Approximately 60 minutes.

Cost: Free

Hosted By: Ben Lorica

Watch the webcast recording

Crate Data is a shared-nothing, fully searchable, document-oriented cluster data store. Today, developers need to "glue" several technologies together to store documents, blobs and support searches and queries in near real time over big data. This isn't always simple or scalable and requires a lot of manual tuning, sharding etc. Crate Data is an open source project that attempts to provide a super simple developers' nirvana - a real time SQL data store for big data - using elasticsearch, Lucene, Netty and Presto. In this webcast we will demonstrate, step-by-step example how a web service can be deployed with the full service stack (data and application) on a single node and then add nodes as needed just by starting them. Crate is self-configuring and self-healing and can be deployed on one device, many devices or the cloud.

About Jodok Batlogg, Co-Founder and CEO, Crate Data

Jodok Batlogg is widely recognized for his expertise in open source and big data. He was an early adopter of cloud services, in 2006, and began working with billions of records early on. Prior to founding Crate Data, a developer of 100% open source data stores for data intensive apps, he was the CTO at StudiVZ (Germany's Facebook), the CTO at Sevenload, and CEO of Lovely Systems. He is also a director of the open source Plone & Zope foundations.

About Ben Lorica

Ben Lorica is the Chief Data Scientist and Director of Content Strategy for Data at O'Reilly Media, Inc.. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, and financial services. He writes regularly about Big Data and Data Science on the O'Reilly Data blog.

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