Stream processing with Apache Kafka, Flink and Spark (2nd-Nov)

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Hotstar India Private Limited

1, 4th A Main Rd · Bengaluru

How to find us

Hotstar office is on the 7th floor. It's in the same building as Boeing.

Location image of event venue

Details

Hi Everyone,

Join us on 2nd-Nov(Saturday) at Hotstar office to discuss stream processing, with talks from Ververica, Razorpay, Hotstar, Qubole, Impetus and GoJek. We will cover Apache Flink, Kafka and Spark in talks, but this will be an open platform to discuss other streaming frameworks too.

We will also be joined by Timo Walther(Apache Flink Committer/PMC) from Ververica(company founded by Flink creators). Timo has helped with SQL efforts in Flink since its inception and he will be answering queries about how it complements or compare with other streaming platforms.

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Agenda
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10:00 - 10:15 -> Registration

10:15 am - 11:05 am -> Stream processing at Hotstar
Speaker: Vinay Patil (https://www.linkedin.com/in/vinaypatil18/), Thejas Babu (https://www.linkedin.com/in/iamthejasbabu/)

11:15 am - 12:05 pm -> Key considerations when using Kafka and Spark streaming to build real-time decision engines (Qubole)
Speaker: Vikram Agrawal (https://www.linkedin.com/in/itsvikramagr/), Prateek Shrivastava (https://www.linkedin.com/in/shrivastavaprateek/)

12:15 pm - 1:05 pm -> Introduction to stream processing with Apache Flink (Ververica)
Speaker: Timo Walther (https://www.linkedin.com/in/twalthr/)

1:05 pm - 1:45 pm -> Lunch break

1:45 pm - 2:35 pm -> Data science at scale with Kafka and Flink (Razorpay)
Speaker: Shashank Agarwal (https://www.linkedin.com/in/shashank734/)

2:45 pm - 3:35 pm -> Real-time credit card approvals using Kafka and Spark (Impetus)
Speaker: Saurabh Dutta (https://www.linkedin.com/in/saurabh-dutta-80)

3:45 pm - 4:30 pm -> Daggers in Gojek - An end to end data aggregator powered by Kafka and Flink (GoJek)
Speaker: Rasyid Hakim (https://www.linkedin.com/in/rasyid-hakim-139258130/), Arujit Pardhan

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Venue
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Hotstar office, 7th Floor, Bagmane LakeView, Bagmane Tech Park, C V Raman Nagar · Bangalore [masked])

Lunch, snacks and beverages will be provided at the venue. Thanks to the Hotstar team for sponsoring venue and lunch.

You can also join remotely through zoom at https://fox.zoom.us/j/993707920

Contact: Saumitra Srivastav ([masked]), Shivam Kapoor ([masked])

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Abstracts
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Topic: Daggers in Gojek - An end to end data aggregator powered by Flink

Kafka is the backbone of Gojek tech and we have more than 300 topics where all the booking, location pings, and log events come in real-time. These real-time events can be utilized to create real-time use-cases like surge pricing, fraud detection, marketing campaign and many more. Most of the use case needs aggregated data in near real-time. Handling all the use cases by creating custom Flink jobs developed by Engineers would not scale. As a solution, we created Daggers which is a DIY real-time data aggregator platform for anyone to use. In this talk, we will talk about the platform architecture, design decisions and how we evolved it to have more than 250 Dagger jobs running today seamlessly.

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Topic: Introduction to Stream Processing with Apache Flink

Apache Flink is a distributed, stateful stream processor. It features exactly-once state consistency, sopGlassbeamhisticated event-time support, high throughput and low latency processing, and APIs at different levels of abstraction (Java, Scala, SQL). In my talk, I'll give an introduction to Apache Flink, its features and discuss the use cases it solves. I'll explain why batch is just a special case of stream processing, how its community evolves Flink into a truly unified stream and batch processor and what this means for its users.

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Topic: Real-time credit card approvals using Kafka and Spark

This session is about the process that happens in the background when a customer applies for a credit card on the website of a large financial institution. We will talk about the domain, use case and back-end implementation of how this major credit card provider is calculating eligibility of a user in real-time using Spark.