lightbend webinars

Revitalizing Enterprise Integration with Reactive Streams

As software grows more and more interconnected, and with several generations of software having to interoperate, a new take on the integration of systems is needed—ad hoc, unversioned, and unreplicated scripts just won’t suffice, and the traditional Enterprise Service Bus (ESB) concept has experienced stability, reliability, performance, and scalability problems.


Concept Drift Monitoring Model Quality in Streaming ML Applications

Most machine learning algorithms are designed to work with stationary data. Yet, real-life streaming data is rarely stationary. Machine learned models built on data observed within a fixed time period usually suffer loss of prediction quality due to what is known as concept drift.


Akka Spark or Kafka Selecting The Right Streaming Engine For the Job

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.


Executive Briefing What Is Fast Data And Why Is It Important

Streaming data systems, so called Fast Data, promise accelerated access to information, leading to new innovations and competitive advantages. These systems, however, aren’t just faster versions of Big Data; they force architecture changes to meet new demands for reliability and dynamic scalability, more like microservices.


Akka and Kubernetes Reactive From Code To Cloud

Akka–the asynchronous, actor-based toolkit for the JVM–is a popular and mature choice for building scalable and resilient Reactive systems in Java or Scala. Kubernetes has rapidly emerged as the de facto standard in the world of container orchestration, with all major cloud providers offering a managed Kubernetes platform.


How To Build Integrate and Deploy Real-Time Streaming Pipelines On Kubernetes

In Fast Data, there is no single technology to rule them all when it comes to implementing multi-component streaming data pipelines into your applications. In order to harness value from real-time data, development teams turn to various technologies–such as Akka Streams, Spark , Kafka, Flink, Kubernetes, and others–depending on their requirements for data ingestion, processing, analysis, and serving.


Streaming Microservices with Akka Streams and Kafka Streams

Kafka Streams is purpose built for reading data from Kafka topics, processing it, and writing the results to new topics. With powerful stream and table abstractions, and an exactly once capability, it supports a variety of common scenarios.


Designing Events-First Microservices For A Cloud Native World

If you’re a human being (or meerkat, chimpanzee, bee, etc.) reading this, then you know that we tend to thrive best by collaborating in a community/system, not in isolation. Similarly, in software development a single service is not terribly useful by itself—services come in systems, and become useful only when they can collaborate as systems.


Running Kafka On Kubernetes With Strimzi For Real-Time Streaming Applications

Apache Kafka, the popular messaging backplane, is traditionally well suited to run on statically defined clusters, but running it on container orchestrated clusters like Kubernetes is becoming more common. The best way to run stateful services on Kubernetes that have complex operational needs (such as Kafka) is to use the Operator pattern.


Migrating From Java EE To Cloud-Native Reactive Systems

A lot of businesses that never before considered themselves as “technology companies” are now faced with digital modernization imperatives that force them to rethink their application and infrastructure architecture. On the path to becoming a digital, on-demand provider, development speed is the ultimate competitive advantage.


Full Stack Reactive In Practice

Curious what goes into a ‘Full Stack Reactive’ project? In this webinar we cover the entire architecture of a Reactive system, from a responsive UI implemented with Vue.js, to a fully event sourced collection of microservices implemented with Java, Lagom, Cassandra, and Kafka.


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

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.