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 Tez: Accelerating Hadoop Data Pipelines
Apache Tez is a DAG execution engine which exists as a super-set of traditional Map Reduce. Tez designed as a replacement computational model for nearly everything that currently uses map-reduce.
The talk is meant to be an introduction to Tez, its architecture and its evolution from traditional map-reduce.
Apache Tez is a modern data processing engine designed for YARN on Hadoop 2. Tez aims to provide high performance and efficiency out of the box, across the spectrum of low latency queries and heavy-weight batch processing. With a clear separation between the logical app layer and the physical data movement layer, Tez is designed from the ground up to be a platform on top of which a variety of domain specific applications can be built. Tez has pluggable control and data planes that allow users to plug in custom data transfer technologies, concurrency-control and scheduling policies to meet their exact requirements. The talk will elaborate on these features via real use cases from early adopters like Hive, Pig and Cascading.
Gopal works on performance problems in hadoop ecosystem. He’s involved with the Stinger effort from Hortonworks to improve the SQL data access layers in Hadoop. He is a contributor to the Apache Hive project and a committer for the Apache Tez project.