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.

Tickets: http://fifthel.doattend.com

Website: https://fifthelephant.in/2014

For queries about proposals / submissions, write to info@hasgeek.com

Theme

  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.

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

Chandraprakash Bhagtani

@cpbhagtani

Dr. Hadoop – Diagnose your Hadoop Jobs

Submitted Jun 13, 2014

Have you faced a problem where you run a job or query on hadoop, which runs very slow, and you have no clue why? You look at your job details on jobtracker and get confused with hundreds of counters and configurations? You really don’t know how to make sense out of it. This is a very common challenge for hadoop beginners specially the analysts or the people coming from RDBMS world. This talk is about the solution that we have built to address this problem.

Outline

This talk is about a tool that we have developed within intuit – Dr. hadoop, which analyzes your job, identifies the areas of improvements and gives recommendations to improve its performance. It collects all the history logs, counters and configuration of your job, applies a set of rules and provides recommendations with suggested values and severity.

Speaker bio

I am a hadoop performance engineer@Intuit. I have been working on hadoop performance for more than 3 years.

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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