Big Data Analytics International Summit 2013

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by Mike Ferguson, Robin Bloor, Philip Howard, Daniel Eklund download a PDF brochure Download Event Brochure


Over the last several years, the challenge of managing and analyzing data to support business needs has been achieved via implementation of Operational Data Stores and Data Warehouses. However today, data requirements have changed and the rate of change required by business is moving faster than many Data Warehouse data models can handle. Also new data sources are often not well understood or unstructured in nature which means that business now needs to flexibility to quickly discover, explore and visualize data without first having to design a data model. In addition, change management is not moving at a pace that many organizations need.

The real rise in data volumes is driven by much higher transaction rates and machine-generated data. Click stream data from web logs and sensor networks are two examples of new data sources that business now wants to analyse. Also the velocity at which this data is being generated is rapidly increasing and more complex varieties of data now what to be analysed.

In order to support these business needs, we have seen the emergence of Big Data technologies including NoSQL Databases, the Apache Hadoop Platform and its technology components, new MapReduce based data discovery and analysis tools, search technologies, stream processing and a massive growth in analytics.

The objectives of this Big Data Analytics Conference is to help you understand these Big Data technologies, and how to integrate them with traditional Data Warehouses and BI platforms in a modern architecture to support end-to-end enterprise analytics and explain how to use them for competitive advantage.



Main Topics

  • Technology disruption: how the technology stack is driving change in IT, BI and Big Data technology
  • An in-depth guide to Hadoop, Pig and Hive
  • SQL on Hadoop – Options for Integrating Big Data into Traditional BI environments
  • NoSQL Databases
  • Graph Databases and Graph Analytics
  • The direction of Analytics and Big Data Analytics
  • Developing Big Data Analytics on the Cloud
  • Approaches to Analysing Multi-structured Data