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 email@example.com
Data Collection and Transport – for e.g, Opendatatoolkit, Scribe, Kafka, RabbitMQ, etc.
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).
Data Processing, Querying and Analysis – Oozie, Azkaban, scikit-learn, Mahout, Impala, Hive, Tez, etc.
Big data and security
Big data and internet of things
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.
Fast Elephant - the Cheeliphant (Cheetah-Elephant)!
In this talk I shall share the spectrum of technologies and the evolution of the Big Data and Analytics space and its associated infrastructures. I shall also touch upon the tips and traps of using these infrastructure and useful thumbrules for designing systems.
The elephant (big data) is catching up increasingly to the cheetah (fast transactional data) and we are increasingly seeing an elephant crossed with a Cheetah - the Cheeliphant! In this talk we shall walk through this evolution compressed into a few years of time, literally a big data revolution. The talk is intended to provide perspective on the ecosystem today and provide some thumbrules for what is appropriate when.
Pre world war 2 the world was a difference in capabilities, but during, world war 2, nations realized that now information had become the weapon. Cryptography and information asymmetry became key and the US Govt invested heavily in Cryptography. Today the weapon of nations in 1942-1980s, is the common place differentiator in business. As Hadoop and Map Reduce have made Big Data accessible to companies and businesses, the frontier of competition has moved from daily reports to machine learning and models and real time analytics.
Every frontier of change leads to new tradeoffs, new cost structures, new gaps and the world of Analytics and Storage has transformed in lockstep; from Hadoop and Map Reduce to Pig to Hive to Storm and Kafka, to Apache Spark and real time gauges and counters in Openstack and statsd to and Complex Event Processing (Esper) and graphite. The Analytics has moved from reporting the blindingly obvious and manual interpretations to alerts and thresholding, to corrective actions and root cause analysis and exception based reporting etc.
As analytics has become a differentiator, the rows are getting wider in the data systems and it has changed how the transaction systems run, what is going into logs and what is going into database. Where we can use traditional databases and where we need to use NoSQL (MongoDB, Cassandra etc.).
In this talk we will flow the data from the transactional system to the analytics system.
Ashok Banerjee is the CTO of EBusiness at Symantec. Ashok has 23 patents approved to date and counting. Prior to Symantec Ashok has led Engineering teams at Google, Twitter, Flipkart etc.
Ashok takes interest in Mathematical Models, Experimentation platforms and Innovation Enablers, Large Data Systems (Databases and alternative databases - NOSQL, Message Systems), Parallel Computing, Distributed Systems, Fault Tolerant Computing, Database, Recommendation Systems, Supply Chain and Mathematical Models and Investments.
On the non-work side Ashok enjoys - sailing, wind surfing, horse riding, german shepherd dogs and soccer.
Experience Summary (reverse chronologically)
Ashok today leads the EBusiness team at Symantec technology team for Data Platform and Analytics at Flipkart and has also led the largest online Supply Chain infrastructure in India (Flipkart) - At Google he led a large scale Datawarehouse infrastructure which converts SQL (approximately) into execution on a platform built on MapReduce, GFS, Columnar compressed data using block oriented computing. This was at the scale of many billion rows added per day (cannot disclose how many billions) - At Google Ashok had led the payment processing infrastructure which processes payments for Adwords, Adsense, Checkout and Google Apps At BEA he worked on WebLogic Server and led infrastructure teams on EJB Container, Web Container, Classloading, Application Deployment within a Server etc. - At Oracle Ashok led the Oracle Application Server Clustering infrastructure and also worked on EJB container and RMI-IIOP Protocols