##Theme and format
The Fifth Elephant 2017 is a four-track conference on:
- Data engineering – building pipelines and platforms; exposure to latest open source tools for data mining and real-time analytics.
- Application of Machine Learning (ML) in diverse domains such as IOT, payments, e-commerce, education, ecology, government, agriculture, computational biology, social network analysis and emerging markets.
- Hands-on tutorials on data mining tools, and ML platforms and techniques.
- Off-the-record (OTR) sessions on privacy issues concerning data; building data pipelines; failure stories in ML; interesting problems to solve with data science; and other relevant topics.
The Fifth Elephant is a conference for practitioners, by practitioners.
Talk submissions are now closed.
You must submit the following details along with your proposal, or within 10 days of submission:
- Draft slides, mind map or a textual description detailing the structure and content of your talk.
- Link to a self-record, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at past editions of The Fifth Elephant.
- If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and documents showing the full workshop plan.
##About the conference
This year is the sixth edition of The Fifth Elephant. The conference is a renowned gathering of data scientists, programmers, analysts, researchers, and technologists working in the areas of data mining, analytics, machine learning and deep learning from different domains.
We invite proposals for the following sessions, with a clear focus on the big picture and insights that participants can apply in their work:
- Full-length, 40-minute talks.
- Crisp, 15-minute talks.
- Sponsored sessions, of 15 minutes and 40 minutes duration (limited slots available; subject to editorial scrutiny and approval).
- Hands-on tutorials and workshop sessions of 3-hour and 6-hour duration where participants follow instructors on their laptops.
- Off-the-record (OTR) sessions of 60-90 minutes duration.
- Proposals will be filtered and shortlisted by an Editorial Panel.
- Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
- Proposers are also encouraged to vote and comment on other proposals submitted here.
We will notify you if we move your proposal to the next round or reject it. A speaker is NOT confirmed for a slot unless we explicitly mention so in an email or over any other medium of communication.
Selected speakers must participate in one or two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.
There is only one speaker per session. Entry is free for selected speakers.
Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.
##Commitment to Open Source
We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.
- Deadline for submitting proposals: June 10
- First draft of the coference schedule: June 20
- Tutorial and workshop announcements: June 20
- Final conference schedule: July 5
- Conference dates: 27-28 July
For more information about speaking proposals, tickets and sponsorships, contact email@example.com or call +91-7676332020.
Real-time Monitoring of Big Data Workflows
Do you want to know the real-time status of your big data job? Not sure of how to collect all the metrics from these jobs and make sense out of them? Want to track and monitor the metrics in real time? Want to track the historical performance of your job? Want to build business reporting dashboards?
The Big Data world has a variety of frameworks to run Hadoop and Spark jobs and tracking these jobs in real time is a huge challenge. The frameworks have kept on increasing but very few attempts have been made to comprehensively monitor these jobs and optimize them.
In this talk, we will discuss a framework to collect and stream big data metrics from different sources in real time, capture them in a metrics OLAP store, run analytics, monitor and alert on them.
For a company like Linkedin, where we run thousands of production BigData workflows, it is important to dedicate enough resources to all the critical workflows. It is also important from a business perspective that these flows don’t waste the resources. In such a scenario it is very crucial to have a system which can easily help visualize all the Workflow metrics and help monitor, debug and optimize the workflows. In addition, it would be really cool if we as developers get automatically alerted when something goes wrong in the workflow like the output records suddenly dropped or there was a spike in the delay and then do slice and dice over the individual delays and figure out what caused it. Whether it was the workflow itself that had an issue or was it due to heavy load on the cluster?
We will discuss the architecture and design for building such a near realtime framework that includes components like Apache Kafka, Samza and OLAP stores. We will also discuss the scope of such a Big Data Metrics store and how consumers like Dr. Elephant can consume from such Metrics Stores and do a lot of analytic processing on them and potentially also auto-tune the jobs based on some models.
Akshay Rai is an engineer at Linkedin working with the Grid team. He is also the lead engineer for the open sourced Dr. Elephant project by Linkedin. He has been working on solutions to improve the developer productivity and building systems to monitor Big Data applications in real time.