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.
Curating A Hunderd Thousand Online Stores Using Storm, ElasticSearch and Etcd
Igor is a platform to curate 100s of thousands of online stores comprised of millions of products while processing billions of product updates. I’ll explore the challenges faced and the architectural decisions that addressed them. I’ll further reveal how Storm, Elasticsearch and Etcd were leveraged to overcome some weaknesses of traditional queue based architectures to deliver low latency event processing with tremendous visibility and fine grained control over the data processing pipeline.
A thematic store is a collection of products that are semantically relevant to a theme, eg: Moto G store: a store for all Moto G variants and their accessories. Rich thematic stores allow online shoppers to navigate effectively and efficiently based on their intent. A valentine’s day store provides a better discovery experience when selecting a valentine’s day gift than browsing through “toys”, “watches”, “lifestyle”.... etc. Curating these stores when one is operating at Flipkart’s scale is no easy task.
At Flipkart, we have a catalog of millions of products, with billions of updates. These updates range from price changes to stock availability, each of these updates could make a product relevant or irrelevant to a particular store. At this scale, building a platform that can curate a hundred thousand dynamic stores in near real time presents massive challenges.
Some of these include
Providing optimal performance in the face of throughput mismatch between source and sink systems
Providing guarantees around processing of updates
Designing the system for multi tenancy
Providing visibility into the data processing pipeline at a store/product level
Providing fine grained control over the data processing pipeline to prioritize processing of selected entities
Ensuring fairness in the data processing pipeline to ensure that each tenant or asset in the system receives its fair slice of processing resources.
Minimizing operational complexity
The talk will address each of them and provide insights into how they were overcome while building Igor.
Basic knowledge of streaming data processing and queue based data processing.
I’m an SDE III at flipkart where I get to play with cool stuff.
I’m a former entrepreneur and have an academic and industrial research background. I’ve publications on varied subjects including, Machine Learning, Semantic Information Retrival, Scalable Image Search Engines and Content Based Image Retrieval.