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.
De-dup on Hadoop
In this talk, I wish to share experiences we had at Intuit in building Master Data Management solution on Hadoop platform. At the core MDM solution consists of fuzzy matching, entity resolution and de-duplication. Solving these patterns on Big Data Platform like Hadoop is the focus of this discussion.
In many enterprises it’s commonly seen that business data has a lot of client, customer, vendor or product lists in different formats and systems, many of which are near duplicates.MDM solutions on RDBMS have been prominent for many years in almost every enterprise to support master data management by removing duplicates, standardizing data and incorporating rules to eliminate incorrect data from entering the system in order to create an authoritative source of master data. MDM on Big data platforms like Hadoop have benefits as well as it’s own set of challenges when compared with the RDBMS counterparts. I will cover them in detail primarily focusing on building this solution on Hadoop.
I am Data Architect at Intuit with 13+ years of experience in BI and Data Analytics. Prior to Intuit, I have worked at Intel, Oracle and EMC applying BI in Manufacturing, Finance and Storage Analytics domain.