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.
Apache Pig Power tools
The objective of this workshop tutorial is to bring Apache Pig users from begginer/intermediate stage to advanced/expert stage.
In this workshop, planning to cover the below list of topics
- A very short introduction to Apache Pig
- Use Grunt shell to work with the Hadoop Distributed File System
- Advanced Pig Operators
- Pig Macros and Modularity features
- Embeding Pig Latin in Python for Iterative Processing and other advanced tasks
- Json Parsing
- XML Parsing
- Extending Pig Latin with Jython
- Pig Streaming
- UDFs Vs. Streaming
- Custom load and store Functions to handle data formats and storage mechanisms
- Single Row Relations
- Python in Pig(Bringing nltk, numpy, scipy, pandas into pig)
- Hue:- With Hartonworks Data Platform
- Pig “Performance Tips”
- We will cover extenal libraries like:- Piggybank, DataFu, DataFu Hour Glass, SimpleJson, ElephantBird
Hortonworks Sandox:- http://hortonworks.com/products/hortonworks-sandbox/
I am Viswanath Gangavram, currently working as Data Scientist in Innovation Labs,7 Inc.
Before 7, I worked in HP Research Labs and IIT Bombay. Did masters in Databases and Information Systems at International Institute of Information Technology, Bangalore(IIIT Bangalore).
Published papers in WWW 2013, COMAD 2009.