data science webinars

Lessons Learned From PayPal Implementing Back-Pressure With Akka Streams And Kafka


Akka Streams and its amazing handling of streaming with back-pressure should be no surprise to anyone. But it takes a couple of use cases to really see it in action - especially in use cases where the amount of work continues to increase as you’re processing it.


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


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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The Basics Of Reactive System Design For Traditional Java Enterprises


Like most things in life, in software there exists an Old and a New way of doing things. The growth of computing power, increase in the sheer number of users, cheaper and more available hardware, and the explosive IoT market mandates that we build our systems using modern methods that diverge from past.


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Exploring Reactive Integrations with Akka Streams Alpakka and Kafka


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.


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Using the Actor Model with Domain-Driven Design (DDD) in Reactive Systems


Is the Actor Model just a new “shiny object” for developers to chase after, a fad soon to be abandoned? In fact, the Actor Model was first designed in 1973–over 20 years before brands like Yahoo!


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Developing Fast Data Architectures with Streaming Applications


Lightbend‘s Fast Data Platform enables users to build streaming data systems quickly and reliably. FDP incorporates a set of best practices that solve common real-world design problems, from cluster creation and management to application development.


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Five Early Challenges Of Building Streaming Fast Data Applications


There is a unification happening between data and microservice architectures: the demand for availability, scalability, and resilience is forcing Fast Data architectures to become like microservice architectures, while organizations building microservices find their data requirements are also evolving.


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Moving from Big Data to Fast Data Heres How To Pick The Right Streaming Engine


For many businesses, the batch-oriented architecture of Big Data–where data is captured in large, scalable stores, then processed later–is simply too slow: a new breed of “Fast Data” architectures has evolved to be stream-oriented, where data is processed as it arrives, providing businesses with a competitive advantage.


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Whats The Role of Machine Learning In Fast Data and Streaming Applications


Machine Learning (ML)–and its subset Deep Learning (DL)–have evolved in the last decade to take an often hidden role in everyday system infrastructures. From self-driving cars to real- time credit card fraud detection to real-time personalization, organizations are using ML to improve customer interactions with systems that can train themselves–using algorithms and historical data–to actively manage complex scenarios without being explicitly programmed.


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A Tale of Two APIs Using Spark Streaming In Production


Fast Data architectures are the answer to the increasing need for the enterprise to process and analyze continuous streams of data to accelerate decision making and become reactive to the particular characteristics of their market.


on-demandiotsoftware developmentakkakubernetesdevOpssparkdata engineeringdata sciencejavakafkascala