Cartonama Conference

Challenges and Best Practises when working with location based data.

Outline

What are the possible ways of storing,retrieving and representing geo-spatial data? What drives the choice of a given representaion of this data? How can small tweaks enhance performance?

The discussion aims to enlighten pros and cons of the various methods of representation when trying to resolve a problem involving geo-spatial data. How would we store and index the data for a location like Bangalore and get relevant information for an area like Koramanagala to serve intelligent information.

We would also like to cover several ways to save infrastructural costs(example of Amazon EMR will be taken).

Our experience is mainly around writing pipelines for processing huge ammounts of spacial data from their raw form to creating relevant reports. We would like to talk about the several performance bottlenecks we faced in representing and maintaining data as well as performance and how we overcame these.

Requirements

Surface Knowledge of Big Data.

Speaker bio

Varun is the Techincal Architect for Big Data at Minjar, Kuliza Technologies. He is working on solving the several problems involved with representation of geo-spatial data and mining this data.

Loves to work on new and exciting challenges. Having worked in diverse technologies involving Rules Management, Virtualization, Mobile etc. in different organizations, he is now working at Minjar, Kuliza Technolgies.