How to build blazingly fast distributed computing like Apache Spark In-house?

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

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Accepting submissions till 15 Jun 2019, 01:00 PM

NIMHANS Convention Centre, Bengaluru

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##The eighth edition of The Fifth Elephant will be held in Bangalore on 25 and 26 July. A thousand data scientists, ML engineers, data engineers and analysts will gather at the NIMHANS Convention Centre in Bangalore to discuss:

Model management, including data cleaning, instrumentation and productionizing data science.
Bad data and case studies of failure in building data products.
Identifying and handling fraud + data security at scale
Applications of data science in agriculture, media and marketing, supply chain, geo-location, SaaS and e-commerce.
Feature engineering and ML platforms.
What it takes to create data-driven cultures in organizations of different scales.

##Highlights:

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.

##Why you should attend?

Network with peers and practitioners from the data ecosystem
Share approaches to solving expensive problems such as cleanliness of training data, model management and versioning data
Demo your ideas in the demo session
Join Birds of Feather (BOF) sessions to have productive discussions on focussed topics. Or, start your own Birds of Feather (BOF) session.

##Full schedule published here: https://hasgeek.com/fifthelephant/2019/schedule

##Contact details:
For more information about The Fifth Elephant, sponsorships, or any other information call +91-7676332020 or email info@hasgeek.com

#Sponsors:

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The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

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How to build blazingly fast distributed computing like Apache Spark In-house?

Submitted May 3, 2019

Session type: Short talk of 20 mins

We at ClustrData are building extremely large scale, extremely cost sensitive analytics solutions for our end user. Being cost sensitive is of utmost importance to us and ease to user is the ultimate goal. We cater to customers who are extremely cost sensitive. Which means whatever we build needs to be super-efficient in terms cost, efficiency and performance. Keeping our design philosophy and cost sensitivity in mind we began exploring various processing frameworks to take care of our processing needs keepingcost in mind. We realised that current state of art technologies like Spark do solve our processing requirements but do not support our cost limitations. In short, if we have
X amount of data and I need to run Spark Compute Cluster with 10 instances continuously for 6 hrs to complete on compute, what if my cost limitations only allow me to run 5Instances for 3 hrs. How can we build a framework which can do that? We need to think out of the box to build something like that. And we would like to share the story of our journey and learnings. And yes, it is possible to do so.

Outline

In this talk we will take care of below questions and explain the same followed by a demo is the system build.
What is business motivation to build Spark like(or better) distributed processing framework in-house?
Why distributed frameworks like Spark will not work for us in long run and why we need something else?
What are basic design layers, data structures and algorithms required to build one such a system?
What are the benchmark results and how it works better than Spark for us?
Demo run of the framework.

Speaker bio

Upendra Singh: Full Stack Data Scientist, 11 years of experience in distributed algorithm development, distributed computing and ML
Lallit Parsai: Data Engineer-2, 6 years experience in Data Engineering and Distributed Systems.