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.
Unified analytics platform for Bigdata
This talk is about a system developed at InMobi to support OLAP data cubes on top of Hive metastore. With this abstraction, users can reference single schema and data stored across diverse storage engine and that users can query data on the logical tables without knowing about schema details like relationships, rollup levels, data location and data types.
Conventional columnar databases (RDBMS) systems lend themselves well for interactive SQL queries over reasonably small datasets in the order of 10-100s of GB, while hadoop based warehouses operate well over large datasets in the order of TBs and PBs and scales fairly linearly. Though there have been some improvements recently in storage structures in the Hadoop warehouses such as ORC, queries over hadoop still typically adopts a full scan approach. Choosing between these different data stores based on cost of storage, concurrency, scalability and performance is fairly complex and not easy for most users. This talk presents Grill, the new analytics platform for InMobi, a system built at InMobi to precisely solve this problem on top Hive metastore.
The Hive metastore in its current state allows users to represent structured data in simple tables. However, it does not allow expressing relationships or richer DWH concepts like facts, dimensions and etc. With Hive data cubes, users can query data stored in HDFS, S3, Redshift etc, with a single query language and schema. Underlying execution engines like Hive, Impala, Shark etc can be plugged in and utilized at run time. The execution engine used is transparent to the user. The system provides a unified logical schema to users consisting of cubes, facts and dimensions; and users can issue queries at a conceptual level without knowing about roll-up intervals, partitions, data types, underlying storage and table relationships; they will be figured out automatically.
Amareshwari is currently working as Architect in platform team at Inmobi, where she works on Hadoop and related projects for data collection and analytics. She is member of Apache Hadoop PMC and is Apache Hive committer. She has been working on Hadoop and its eco system since 2007. Prior to Inmobi, she was working with Yahoo! in core Hadoop team. She holds bachelor’s degree in computer science and engineering from National institute of technology, Waragal, India; and master’s degree in Internet science and engineering from Indian Institute of Science (IISc), Bangalore, India.
- Linked profile - http://www.linkedin.com/pub/amareshwari-sriramadasu/18/124/355
- Grill documentation - http://inmobi.github.io/grill/
- Slides presented at ApacheConf - www.slideshare.net/amarsri/datacubes-in-apache-hive-at-apachecon
- Slides prepsented at Hadoop Summit - http://www.slideshare.net/amarsri/grill-at-hadoopsummit