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 firstname.lastname@example.org
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.
Scaling with Queues
Share the experience of using queues based backend infra architecture for scalability, failover and data accuracy.
Talk on design and implementation of a distributed queue based scalable CQRS architecture at Wingify for doing A/B testing analytics, data acquisition and distributed processing using RabbitMQ, OpenResty, Lua, Python, Redis, C++/Thrift and RocksDB.
The talk on architecture will be around distributed queue and how queueing as a scaling solution works and the rest of the talk will cover infra and scalability challenges we have solved using this architecture at Wingify where we use it for analytics, data processing, database updates and for supporting niche features within the VWO app that uses a homegrown high volume writes db called HarvestDB based on RocksDB.
Speaker is a systems engineer at Wingify, a Delhi based bootstrapped startup that develops the A/B testing tool -- Visual Website Optimizer (VWO). He is an opensource enthusiast and committer with Apache CloudStack and VideoLAN VLMC. More on: bhaisaab.org