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.
Ten things to consider for Interactive Analytics on high volume, write-once workloads
With the advance of No-SQL and big data, there has been an explosion of database technologies. Each of them are best suitable for certain kind of work loads. For applications such as log analysis, sensor data analytics, genome data analytics, what is the framework to evaluate the best suitable databases. This session explains core technologies which benefit write-once workload and mapping to various industry databases of hadoop and related technologies.
CONTEXT – Write once data load - Ex. Time-series data. Which Database?
SSD is Good
MPP is Good
Columnar is Good
Logical Partition is Good
Data Skew Partition is Good
Search Engine Index could lead to Index Explosion
Concurrent Users First, Single Query Performance Next
High Throughput File level Snapshot Loading
Calculate cost upfront
Data Structure makes a Big Difference
CTO and Co-Founder at Bizosys Technologies since 2009
Created HSearch – a Real-time, distributed search and analytics engine built on Hadoop platform
Passion on distributed systems and data structures
Speaker at Fifth Elephant 2013, Microsoft Teched 2012 (Hadoop on Azure), Yahoo Hadoop India Summit 2011
Developed partitioning, read optimized data structures modules for HSearch.
Worked with a range of search products including Lucene, Solr, Endeca and FAST
Abinash is an engineering graduate of NIT, Raurkela