Webinar

Modernizing Infrastructures for Fast Data with Spark, Akka, Kafka, Cassandra & Mesos

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Modernizing Infrastructures for Fast Data with Spark, Akka, Kafka, Cassandra & Mesos

With Dean Wampler

fast-data spark akka

The need to process data more quickly is morphing Big Data architectures into Fast Data architectures. This session discusses the forces driving this trend and the most popular tools that have emerged to address particular design challenges.

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It’s become clear to many business that the ability to extract real-time actionable insights from data is not only a source of competitive advantage, but also a way to defend their existing business models from disruption. So while legacy models such as nightly batch jobs aren’t disappearing, an era of fast, streaming data (aka “Fast Data”) is upon us, and represents the state of the art for gaining real-time perishable insights that can then be used to serve existing customers better, acquiring new markets and keep the competition at bay.

That said, distributed, Fast Data architectures are much harder to build, and carry their own set of challenges. Enterprises looking to move quickly are presented with a growing ecosystem of technologies, which often delays fast decisions and provides its own set of risks:

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  • e best patterns and practices for streaming applications?

In this webinar with Sean Glover, Senior Consultant at Lightbend and industry veteran, we examine the rise of streaming systems built around Spark, Mesos, Akka, Cassandra and Kafka, their role in handling endless streams of data to gain real-time insights. Sean then reviews how the Lightbend Fast Data Platform (FDP) brings them together in a comprehensive, easy to use, integrated platform, which includes installation, integration, and monitoring tools tuned for various deployment scenarios, plus sample applications.

Exploring Reactive Integrations with Akka Streams, Alpakka and Kafka

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Exploring Reactive Integrations with Akka Streams, Alpakka and Kafka

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Audience: Architects
Technical level: Introductory

Since its stable release in 2016, Akka Streams is quickly becoming the de facto standard integration layer between various Streaming systems and products. Enterprises like PayPal, Intel, Samsung and Norwegian Cruise Lines see this is a game changer in terms of designing Reactive streaming applications by connecting pipelines of back-pressured asynchronous processing stages.

This comes from the Reactive Streams initiative in part, which has been long led by Lightbend and others, allowing multiple streaming libraries to inter-operate between each other in a performant and resilient fashion, providing back-pressure all the way. But perhaps even more so thanks to the various integration drivers that have sprung up in the community and the Akka team—including drivers for Apache Kafka, Apache Cassandra, Streaming HTTP, Websockets and much more.

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Distributed Systems Done Right: Why Java Enterprises Are Embracing The Actor Model

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Most likely, your job is heavily focused on helping your organization modernize for the digital era. As the days of purely Object-Oriented Programming and related frameworks come to a close, enterprises migrating to distributed, cloud infrastructures are embracing a different approach: the Actor Model.

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Created by Carl Hewitt in 1973, Forrester Research notes, “the Actor model is seeing renewed interest as cloud concurrency challenges grow.”

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Our goal is twofold: provide you with a comprehensive review of the Actor Model, and give you the resources you need to help others learn why enterprises like Walmart, Intel, Samsung, IBM, Norwegian Cruise Lines and HSBC are committed production users of Akka, the JVM-based toolkit built on the Actor Model.

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  • Why failure detection and failure recovery is an architectural feature of Actor systems
  • An example of using Actors to build an Internet of Things (IoT) application

Fast Data: Selecting The Right Streaming Technologies For Data Sets That Never End

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Fast Data: Selecting The Right Streaming Technologies For Data Sets That Never End

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Why have stream-oriented data systems become so popular, when batch-oriented systems have served big data needs for many years? Batch-mode processing isn’t going away, but exclusive use of these systems is now a competitive disadvantage. You’ll learn that, while fast data architectures are much harder to build, they represent the state of the art for dealing with mountains of data that require immediate attention.

In this webinar, Lightbend’s Big Data Architect, Dr. Dean Wampler, examines the rise of streaming systems for handling time-sensitive problems. We’ll explore the characteristics of fast data architectures, and the open source tools for implementing them.

We’ll also take a brief look at Lightbend’s upcoming Fast Data Platform (FDP), a comprehensive solution of OSS and commercial technologies. FDP includes installation, integration, and monitoring tools tuned for various deployment scenarios, plus sample applications to help you sort out which tools to use for which purposes.

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The term 'streams' has been getting pretty overloaded recently–it's hard to know where to best use different technologies with streams in the name. In this talk by noted hAkker Konrad Malawski, we'll disambiguate what streams are and what they aren't, taking a deeper look into Akka Streams (the implementation) and Reactive Streams (the standard).

You'll be introduced to a number of real life scenarios where applying back-pressure helps to keep your systems fast and healthy at the same time. While the focus is mainly on the Akka Streams implementation, the general principles apply to any kind of asynchronous, message-driven architectures.

Audience: Architect
Technical level: Intermediate

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