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Overview for stream processing


MetaConfig driven FeatureStore with Feature compute & Serving Platform powering Machine Learning @MakeMyTrip

MakeMyTrip is India’s #1 online travel platform having more than 70% of the traffic from mobile apps embarked on a journey to revolutionize its customer experience by building a scalable, personalized, machine learning based platform which powers onboarding, in-funnel and post-funnel engagement flows, such as ranking, dynamic pricing, persuasions, cross-sell and propensity models.

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Apache Flink 101 - the rise of stream processing and beyond

This talk is about some Flink use cases and basic requirements of stream processing, and how Flink fills the gaps and stands out with some of its unique core building blocks, like pipelined execution, native event time support, state support, and fault tolerance.

There is also a highlight of how Flink is going beyond stream processing into areas like unified data processing, enterprise intergration, AI/machine learning  especially online ML, and serverless computation, and how Flink fits with its distinct value.

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How to performance-tune Spark applications in large clusters

Omkar Joshi a senoir software engineer at Uber discusses a new Spark ingestion system known as Marmaray. This new system has been designed to ingest billions of Kafka messages at intervals of 30 minutes. 

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The Need for Speed – Data Streaming in the Cloud with Kafka®

Running Kafka on Kubernetes is becoming more and more popular. Frank Pientka, Principal Software Architect, Materna Information & Communications SE introduces a setup, used components and recommendations from an own project with Kafka on Kubernetes.He shares the lessons learned from this still evolving field. 

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