20131011 Design Patterns for Big Data Architecture: Best Strategies for Streamlined [Simple, Powerful] Design - Minnesota - MinneAnalytics
- 1,365 views
The concerns of large scale distributed computing now go far beyond storage solutions to use a wide range of big data analytics, machine learning and interactive applications. The scale of projects is huge, the components vary from real-time to interactive to batch solutions, and the architecture may become very complex to accommodate these needs. How do you make the best choices to keep architectural design for these projects simple yet powerful?
This presentation describes new innovations for key big data architecture design patterns, from the technical details to real world use cases. Wouldn’t you like to be able to stream real-time data or query directly to a cluster? To simplify deployment of machine learning models in production? To easily incorporte web protocols into designs based on distributed data storage? This talk gives practical guidelines to show you how to efficiently integrate Hadoop-based computing with widely needed components that include real-time approaches such as Storm, search and index technology Solr, machine learning with Apache Mahout or enterprise solutions, and more.
- Total Views
- Views on SlideShare
- Embed Views