What are your users doing on your website or in your store? How do you turn the piles of data your organization generates into actionable information? Where do you get complementary data to make yours more comprehensive? What tech, and what techniques?
The Fifth Elephant is a two day conference on big data.
Early Geek tickets are available from fifthelephant.doattend.com.
The proposal funnel below will enable you to submit a session and vote on proposed sessions. It is a good practice introduce yourself and share details about your work as well as the subject of your talk while proposing a session.
Each community member can vote for or against a talk. A vote from each member of the Editorial Panel is equivalent to two community votes. Both types of votes will be considered for final speaker selection.
It’s useful to keep a few guidelines in mind while submitting proposals:
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Describe how to use something that is available under a liberal open source license. Participants can use this without having to pay you anything.
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Tell a story of how you did something. If it involves commercial tools, please explain why they made sense.
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Buy a slot to pitch whatever commercial tool you are backing.
Speakers will get a free ticket to both days of the event. Proposers whose talks are not on the final schedule will be able to purchase tickets at the Early Geek price of Rs. 1800.
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The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
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qubole
@qubole
Messaging architecture at Facebook
Submitted Jun 10, 2012
Section:
Industry & Business
Technical level:
Advanced
Session type:
Lecture
The audience will learn design options for building large scale messaging system and how technical, philosophical and organizational considerations surrounding these played out at Facebook.
Outline
Building a scalable messaging backend for a Billion user is Hard. This talk covers the initial design days when the Facebook messaging architecture was being decided.
Should back-end stores be eventually or strongly consistent? How much availability is highly available? Should we build services or components? How big does a piece of software have to be before it stops being a component. How can a back-end be built for an application that’s yet to be designed? Is it possible to simulate a billion user workload - and does it help?
The issues faced by the design team ranged from the technical to the philosophical - to ultimately the practical. This talk will explore some of these areas.
Requirements
Given the limited time - it would not be possible to introduce background material. A grasp of scalable key value stores, CAP theorem and eventual consistency, reliable storage systems would be useful in getting more out of the talk.
Speaker bio
Joydeep is a co-founder at Qubole and heads their India development team. Prior to starting Qubole - Joydeep worked at Facebook where he boot-strapped the data processing ecosystem based on Hadoop, started the Apache Hive project and led the Data Infrastructure team. Joydeep was a key contributor on the Facebook Messages architecture team that brought Apache HBase to Facebook and to the transactional and reporting backends for Facebook Credits. He has been a driver for other important sub-projects in the Hadoop ecosystem - like the FairScheduler and RCFile. Joydeep studied Computer Science at IIT-Delhi and University of Pittsburgh and started his career working on Oracle’s database kernel and building highly available and scalable file systems at Netapp. In between - he has played founding roles in storage and advertising startups. He cut his teeth building data driven applications as the lead engineer on Yahoo’s in-house Recommendation Platform.
Joydeep holds numerous patents, has many published papers and has been both speaker and panelist at Hadoop summits and at other Silicon Valley conferences.
Comments
Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
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