The Fifth Elephant 2017
On data engineering and application of ML in diverse domains
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
On data engineering and application of ML in diverse domains
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
##Theme and format
The Fifth Elephant 2017 is a four-track conference on:
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:
##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:
##Selection Process
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.
##Travel grants
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”.
##Important Dates:
##Contact
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.
Hosted by
Matild Reema
@matild_reema
Submitted Jun 9, 2017
In this talk I will talk about how we used Apache Spark to build a self service data platform at Indix that helped democratise access to several datasets at Indix to our customers and the internal engineering and data science teams. I will also share some of the lessons learnt while optimizing performance and tuning Spark jobs that run on these datasets.
Indix is building the world’s largest catalog of structured product information and to achieve this mammoth goal we’ve built various data pipelines using Hadoop, Kafka, Akka Streams and Apache Spark.
We started using Apache Spark well before its 1.0 release with mixed success but over the last couple of years, it has become the framework of choice for building data pipelines at Indix.
My talk will consist of two sections
Every day the Indix Data platform teams generate several terabytes of data, what we call as internal datasets. This includes crawled HTML pages, semi-structured product data extracted from these pages and the output of several ML algorithms on these datasets. These datasets are available on HDFS, S3 or on Kafka topics, have different schemas and are encoded in Avro, Thrift, CSV or Parquet formats.
18 months ago, accessing these datasets, processing them or running ad-hoc queries on them was a painful process and users had to be conversant with all these tools and technologies to make sense of it. Since then we have built a data platform on top of Apache Spark to make it easier for anyone to access these anytime. We used our own abstractions and married them with the abstractions like Datasets of Apache Spark to provide a seamless and self service experience to our internal teams. The first section will state the problem, the abstractions built and finally show a demo of the tool we built.
If you have tried your hands at tuning Spark jobs on large datasets, you would know that it is a dark art as you run into a number of hidden Spark pitfalls that only surface once your datasets grow into several terabytes and your jobs are running across 100s of executors. One also needs a deeper understanding of Sparks internals especially around memory management, shuffle and partition management when running joins or aggregates. The second section of the talk will talk about the common Spark piftalls, the scenarios they usually occur in and best pratices to adopt for optimal performance.
Whether you are just starting out with Spark or you are a seasoned developer, by the end of the talk, you should learn how to build data products on top of Apache Spark with tips and tricks to help make your jobs run faster.
I work as a software engineer at Indix on the data platform team where I am part of the core team that built and manages the self service data platform on Apache Spark.
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
Hosted by
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