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.
Live analytical dashboards at scale - SQL style
How to build a real-time, analytical dashboads that can enable business take decisions at scale? There are various technologies out there that fill one or the other use case - right from horizontally scalable queues such as kafka, stream processing systems such as storm, data stores such as openTSDB and druid that can provide dimensional lookup on large amount of data and visualisation libraries such as d3, cubism to view them. But there is a lot more that is never discussed if not for the details. In a service-oriented architecture, where dimensions and measures are coming from different sources, where some dimension is larger than the number of seconds in a year (for those who are wondering it is ~32 M), ensuring liveness and correctness at every minute is what Fireball is all about.
Fireball is a stream processing engine at Flipkart. It powers real time analytical dashboards to enable business take time-sensitive decisions, at scale. Fireball can process millions of events (with flexible, json-like schema) per hour that require:
* executing custom process (usually SQL-like) to derive business metrics from the incoming events
* over large number of dimensions (on an average 10 dimensions for each measure)
* with very low latency and ensuring correctness all the time (enabling time-sensitive decision making)
So how do you build such a system? How do you store such a large amount of time-series data to ensure roll-ups, drill-downs on different dimensions? In this talk we’ll go over the transformation of a standard stream processing platform and a CEP library into Fireball.
I am Architect @ Flipkart and am part of the Data Platform effort.