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.
Lessons from Elasticsearch in production
This talk is for people who are planning to use Elasticsearch in their next project.
Elasticsearch is one of the core pieces of infrastructure at Helpshift, a mobile-first CRM product.
We have had mistakes and lessons learned in production, and we would like to share that so that others have a smoother experience with Elasticsearch.
The customer-facing side of Helpshift product is a simple chat feature within the app using the Helpshift mobile SDK. The business-facing side is a complex agent dashboard that helps the agent in processing as many issues as quickly as possible. We will be focusing on this business-facing side, the designs we built on top of Elasticsearch and the problems we faced and how we went about solving them, a few of them are:
- Architecture 101 - Importance of separating master-only and data-only nodes, etc.
- How we index documents for each customer - the flaw in having one index per customer, and possible solutions
- Multilingual data - the importance of the phonetic plugin
- Complex views - the importance of understanding filters and how to combine them
- The importance of benchmarking - how we implemented using percolators for live notifications of new issues
- Restarts & Upgrades - the importance of disabling shard allocation and clustering
- Bulk Indexing - the importance of controlling replica count, etc.
- Runtime debugging - the importance of cat APIs, etc.
Preferably, audience has some basic understanding of what Elasticsearch does.
I work in the backend team at Helpshift.com, a customer service platform for mobile apps, our customers include Flipboard, Supercell, Flipkart, and several others.
I have also written a couple of Creative Commons-licensed books - A Byte of Python and A Byte of Vim.