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.
BDAS, the Berkeley Data Analytics Stack
This talk is an introduction to the features about the next generation, open source data analysis stack developed by UC Berkeley AMPLab.
BDAS is made up of multiple components and compatible with the Hadoop stack
- Spark, a high speed cluster computing system with an ability to perform computations in memory.
- Mesos, a cluster manager that provides efficient resource isolation and sharing across distributed applications
- Tachyon, a fault tolerant distributed file system enabling reliable file sharing at memory-speed across cluster frameworks
- MLBase, a platform for implementing and consuming Machine Learning techniques at scale
- Shark, a port of Apache Hive onto Spark that is compatible with existing Hive warehouses and queries
- Spark Streaming extends Spark to build scalable fault-tolerant streaming applications
- GraphX, extends Spark with an ability to deal with structured graph data
Participants should have basic understanding about Big Data concepts and Hadoop.
Working on software for more than 15 years, with a focus towards improving performance and optimization of applications and algorithms. Interests include Big Data, parallelism, algorithm optimization etc...