The Fifth Elephant 2014

A conference on big data and analytics

In 2014, infrastructure components such as Hadoop, Berkeley Data Stack and other commercial tools have stabilized and are thriving. The challenges have moved higher up the stack from data collection and storage to data analysis and its presentation to users. The focus for this year’s conference on analytics – the infrastructure that powers analytics and how analytics is done.

Talks will cover various forms of analytics including real-time and opportunity analytics, and technologies and models used for analyzing data.

Proposals will be reviewed using 5 criteria:
Domain diversity – proposals will be selected from different domains – medical, insurance, banking, online transactions, retail. If there is more than one proposal from a domain, the one which meets the editorial criteria will be chosen.
Novelty – what has been done beyond the obvious.
Insights – what insights does the proposal share with the audience that they did not know earlier.
Practical versus theoretical – we are looking for applied knowledge. If the proposal covers material that can be looked up online, it will not be considered.
Conceptual versus tools-centric – tell us why, not how. Tell the audience what was the philosophy underlying your use of an application, not how an application was used.
Presentation skills – proposer’s presentation skills will be reviewed carefully and assistance provided to ensure that the material is communicated in the most precise and effective manner to the audience.



For queries about proposals / submissions, write to


  1. Data Collection and Transport – for e.g, Opendatatoolkit, Scribe, Kafka, RabbitMQ, etc.

  2. Data Storage, Caching and Management – Distributed storage (such as Gluster, HDFS) or hardware-specific (such as SSD or memory) or databases (Postgresql, MySQL, Infobright) or caching/storage (Memcache, Cassandra, Redis, etc).

  3. Data Processing, Querying and Analysis – Oozie, Azkaban, scikit-learn, Mahout, Impala, Hive, Tez, etc.

  4. Real-time analytics

  5. Opportunity analytics

  6. Big data and security

  7. Big data and internet of things

  8. Data Usage and BI (Business Intelligence) in different sectors.

Please note: the technology stacks mentioned above indicate latest technologies that will be of interest to the community. Talks should not be on the technologies per se, but how these have been used and implemented in various sectors, enterprises and contexts.

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Vinayak Hegde

Vinayak Hegde


How to build a Data Stack from scratch

Submitted May 22, 2014

This talk will cover a framework for thinking about the analytics data stack. What are the things to consider when building a data stack from scratch. How to choose the right software for your stack whether it is visualisation, analytics or storage ? It will talk about the relations between different techniques for extracting insights outs of raw data. I will draw upon examples from my experience of building 3 different data stacks in 3 different industry verticals (Networks, Advertising and Customer Support) and what I learnt from each.


In the talk I will talk about my experience of how to build a data stack from stratch. I have built a big data analytics stack at Akamai and Inmobi before and am currently building one now at Helpshift. These are three different domains - Content Delivery Networks (Akamai), Mobile Advertising (Inmobi) and now Customer Service (Helpshift).

More specifically, my talk will try to cover these questions and more

  • What are the different components of an analytics stack and what function does each layer have ?
  • How do you choose the right software for different layers of your analytics data stack ?
  • Do you use real-time analytics or batch processing is right for you ? What are the costs/benefits of both ?
  • What is the relation between statistical and probabilistic techniques ? Which to choose when ?
  • How to decide on the right structure and storage for your data and how they influence your analytics stack ?
  • How to decide on the right metrics for your business and how they influence your analytics stack ?

I will use specific industry examples how each of these questions were answered differently in different contexts. I will also talk the factors that influenced these decisions and how they influenced the final output and architecture.


An open mind and some understanding of mathematics and computer science.

Speaker bio

Vinayak is an early adopter of technologies having worked across diverse and complex computer systems including embedded systems, networking, large-scale distributed systems and data-processing systems. He has more than a decade of experience in hardcore product development & software/deployment architecture.

He has led engineering teams at Akamai, Inmobi and Helpshift to build big data stacks from scratch. He organised one of the first Cloudcamps and Barcamps in India. He co-founded Headstart, a grass-roots community driven by volunteers for helping startups. Other than his interests in tech and startups, he is an avid traveller and amateur photographer.



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All about data science and machine learning