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

Sunil Sayyaparaju

@sunils

Overcoming problems that you will face when trying to break speed limit

Submitted Jun 15, 2014

It is everyone’s continuous quest to improve the speed at which we do things. What is fast in the past is no longer fast. We need to continuously improve things. In those efforts, we face problems. We also get new opportunities because of the evolving technologies. This talk is to share our knowledge about the opportunities we used and how we overcame some of the problems.

Outline

Scaling can be done in two ways
1. Vertical Scaling: Vertical Scaling is about how much more we can achieve on a single system. Its more about better utilization of the resources CPU, Disk I/O, Network I/O, RAM, Interrupts etc. All these resources should be given their due respect. The newer kernels and libraries over them give better hooks at controlling and using these resources.
2. Horizontal Scaling: Horizontal Scaling is about using multiple machines as a single unit (cluster) to split the problem and solve them in parallel. All the distributed systems like Hadoop, NoSQL databases etc fall into this category. Distributed systems does not magically give speed. A few principles and disciple needs to be followed or else a distributed system may perform worse than a single system.

We will discuss these two aspects in the talk

Speaker bio

Sunil Sayyaparaju, Engineering Lead at Aerospike, has over 9 years experience working on different types of SQL RDBMS solutions, such as single machine (monolithic), in-memory, distributed shared-disk, and distributed shared-nothing architectures with emphasis in transaction management, storage, access, performance tuning, and recovery areas.

Sunil currently leads Aerospike’s Bangalore office, working on their distributed shared-nothing NoSQL solution. Aerospike is a high-performance, self-balancing, immediately consistent, distributed NoSQL database. Aerospike also has an add on product for replication across data-centers over WAN which supports different complex topologies.

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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