Jul 2019
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26 Fri 09:20 AM – 05:30 PM IST
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Accepting submissions till 15 Jun 2019, 01:00 PM
Jul 2019
22 Mon
23 Tue
24 Wed
25 Thu 09:15 AM – 05:45 PM IST
26 Fri 09:20 AM – 05:30 PM IST
27 Sat
28 Sun
Accepting submissions till 15 Jun 2019, 01:00 PM
1. Meet Peter Wang, co-founder of Anaconda Inc, and learn about why data privacy is the first step towards robust data management; the journey of building Anaconda; and Anaconda in enterprise.
2. Talk to the Fulfillment and Supply Group (FSG) team from Flipkart, and learn about their work with platform engineering where ground truths are the source of data.
3. Attend tutorials on Deep Learning with RedisAI; TransmorgifyAI, Salesforce’s open source AutoML.
4. Discuss interesting problems to solve with data science in agriculture, SaaS perspective on multi-tenancy in Machine Learning (with the Freshworks team), bias in intent classification and recommendations.
5. Meet data science, data engineering and product teams from sponsoring companies to understand how they are handling data and leveraging intelligence from data to solve interesting problems.
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Hosted by
Agam Jain
@agamjain
Submitted Apr 15, 2019
This talk is about sharing our learnings and some best practices we have built over the years working with massive volume and every changing schema of data.
What we are not going to discuss is specifics of what actually technological choices we made. Or, how we scaled out system 10x year on year. Or, how we brought down the latency in processing of our data to half.
Zapr has profiled millions of users for tv consumption and in part we had to build our data processing pipeline from scratch. Initially, We started off doing it all the wrong way first by adding fields over time. As a results it came to a point where it was impossible to manage or keep track of fields that were present in data. Core concept in this talk is around how we should model the data flowing in the pipelines and the advantages it gives both from a Business as well as a technical perspective.
This talk should help anyone new into building data processing pipelines in their organization to be future proof and vary of pitfalls when dealing with data schemas which are evolving
Folks who are already doing it and have built expertise around it will be able to relate and get another perspective on how to manage data flowing in their pipelines
The flow would look like this
Discuss the gains from implementing contract control for any data that flows in the data pipeline
Additional Advantages
* Cost wise
* Data cleaning
* Data consistency
* Linear pipeline
I work as a Tech Architect at zapr. Working closely with data engineering teams and more specifically drive initiatives to help improve the quality of the data. In my spare time i like to read about how lot of different organizations are solving new type of problems, listen to lot of podcasts and watch football
https://docs.google.com/presentation/d/1AYRDBXkXUu-lo-4U8meh3YX4DYLZyuXlR2izcVR9T8Y/edit?usp=sharing
Jul 2019
22 Mon
23 Tue
24 Wed
25 Thu 09:15 AM – 05:45 PM IST
26 Fri 09:20 AM – 05:30 PM IST
27 Sat
28 Sun
Accepting submissions till 15 Jun 2019, 01:00 PM
Hosted by
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