stream processing


Emergence of Real-Time Analytics: Real-time Analysis of Customer Financial Activities With Apache Flink

Location: Salon A
April 12th, 2016
1:30 PM - 2:30 PM

People's financial activities with banks are increasingly migrating to digital platforms. Banks, which are large institutions that move money are transforming into Software Engineering Companies. At the core of modern banks is a large network of systems and platforms that capture, collect, process and analyze the digital data. Collecting and analyzing customers' activities in real-time is critical for modern financial institutions to succeed. In this talk we present a business use case where Capital One needs to process customer activities real-time and react to events appropriately as needed. We then present our experience in building a real-time analytics application that
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Srinivas Palthepu

Senior Manager Big Data Engineering, CapitalOne

NoLambda: A new architecture combining streaming, ad hoc, machine learning, and batch analytics

Location: Salon A
April 11th, 2016
1:30 PM - 2:30 PM

In today’s world of exploding big and fast data, developers who want both streaming analytics and ad hoc, OLAP-like analysis have often had to develop complex architectures such as Lambda—a path for fast streaming analytics using NoSQL stores such as Cassandra and HBase with a separate batch path involving HDFS and Parquet. While this approach works, it involves too many moving parts, too many technologies for ops, and too many engineering hours. Helena Edelson and Evan Chan highlight a much simpler approach to combine streaming and ad hoc/batch analysis using what they call the NoLambda stack (Apache Spark/Scala, Mesos, Akka,
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Evan Chan

Creator, FiloDB

From Concurrent to Parallel: Understanding Parallel Stream Performance in Java SE 8

Location: Salon C
April 11th, 2016
4:00 PM - 5:00 PM

As core counts continue to increase, how we exploit hardware parallelism in practice shifts from concurrency -- using more cores to handle more user requests -- to parallelism -- using more cores to solve data-intensive problems faster. This talk will explore the different goals, tools, and techniques involved between these various approaches, and how to analyze a computation for potential parallelism, with specific attention to the parallel stream library in Java 8. Brian's talk is now available on the Chariot Solutions site. Slides Screencast

Brian Goetz

Java Language Architect, Oracle

Demystifying Stream Processing with Apache Kafka

Location: Salon A
April 12th, 2016
11:30 AM - 12:30 PM

The concept of stream processing has been around for a while and most software systems operate as simple stream processors at their core: they read data in, process it, and maybe emit some data out. So why are there so many stream processing frameworks, all with different terminology, and why does it seem so complex to get up and running? What benefits does each stream processing system provide, and more importantly, what are they missing? This presentation will start by abstracting away the individual frameworks and describe the key features and benefits that stream processing frameworks provide. These core features
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Ewen Cheslack-Postava

Engineer, Confluent