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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Vaidik Kapoor

Vaidik Kapoor


Using Elasticsearch for Analytics

Submitted May 18, 2014

At Wingify, we have built a system to process and store analytics data for our customers, which they can use to slice and dice the data to make more meaningful reports. This talk is about how we solved this problem and how we used Elasticsearch to solve this problem at our scale rather quickly. Audience will take away some of the data problems they can quickly solve with Elasticsearch.


At Wingify, we collect data for website and mobile A/B testing campaigns created by our customers, store, process and crunch it to make it usable by our customers in the form of various reports that they use for Conversion Rate Optimization. This data is critical for our customers to make engineering, design and business decisions to improve their conversions and achieve their business goals. Being able to generate custom reports according to our customers’ requirements with the ability to slice and dice the data to get more targeted and meaningful reports is an important feature of the core of our application. And the enormous amount of data generated by campaigns created by thousands of customers makes this problem even more difficult as we have to carefully process the data keeping in mind the current and the future needs of the application, store it so that our users can play with it with the utmost flexibility and serve reports created using this data as fast as possible.

This talk will focus on how we discovered and used Elasticsearch to quickly prototype and use it to solve the described problem, how we took our implementation from prototype to production and the challenges we faced along the way:

  1. A very brief introduction to Elasticsearch and its amazing features that make it a really good system to use for quick prototyping to see how it may solve your problems.
  2. Architecting the data pipeline for fast writes and accurate updates with the ability to control what to process and write.
  3. Planning for fast reads and aggregation of large data sets.
  4. Designing the system for accuracy and maintaining data consistency.
  5. Elasticsearch scales when planned. Planning early for scaling needs.
  6. Preparing to handle problems at scale and fire fighting.
  7. The things about Elasticsearch we learned and what helped us the most.
  8. Best practices and tips from our experience of using Elasticsearch for all this.

Speaker bio

I am a software engineer based out of New Delhi, working for Wingify, a Delhi based bootstrapped startup that develops the A/B testing tool - Visual Website Optimizer (VWO). At Wingify, I am primarily focused on services, scalability and infrastructure engineering, which also happens to involve working with data and analytics and all the problems that come with it. I am an open-source enthusiast. In my free time, I evangelize and try to organize local meetups and watch movies.


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All about data science and machine learning