Develop highly scalable applications on Google App Engine part 2: Handling larger datasets using Google Map Reduce.
by Rohit Sharma (speaking)
Objective
- Dive into Map Reduce, a distributed computing model for processing large datasets.
- Leverage Google Map Reduce for distributed data processing in your application.
Description
Data Curation and Processing are amongst the major functional aspects of any web application.
Due to its ability to scale while being reliable, Map Reduce has found its application in a wide range of functional aspects like sorting, searching, machine learning, statistical analysis etc. In this session we will be exploring the Google Map Reduce to perform data processing on large data sets. Since customers are billed for each atomic unit (it can be data or an operation) on the App Engine, using Google Map Reduce for data processing also helps in saving runtime costs.
Speaker bio
Rohit is a developer working at Google as a consultant from Globallogic having an experience of 5 years in developing web applications using Python and Java. His current role involves collaborating and developing with Google’s developers across geographies and works extensively on the App Engine and Python.
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Hi Rohit,
Is this a workshop or a talk? Can you combine both parts and submit as a workshop, if that is more appropriate?
Also, please add links to your previous presentations, personal website, github profile etc.
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Hi Anand,
I think we are mistaken here. As per conference rules workshops are 3 hour long. I think it won't take 45-50 mins for me to go through my session. It would be good if I change it back to session.
This is continuation of scaling an App Engine application and part 1 of this is also a session (http://in.pycon.org/funnel/2013/17-develop-highly-scalable-applications-on-google-app-engine-part-1-exploiting-the-intricacies-of-storage-options-and-making-your-application-api-ready).
Please suggest.
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Hi Anand,
I have changed this to workshop.
I don't have previous presentations etc.