By now, you’ve probably heard of Akka, the JVM toolkit for building scalable, resilient and resource efficient applications in Java or Scala. With over 12 open-source and commercial modules in the toolkit, Akka takes developers from actors on a single JVM, all the way out to network partition healing and clusters of servers distributed across fleets of JVMs.
Streaming data systems, so called “Fast Data”, promise accelerated access to information, leading to new innovations and competitive advantages. But they aren’t just “faster” versions of Big Data systems. They force architecture changes to meet new demands for reliability and dynamic scalability, more like microservices.
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.
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.
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.
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.
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–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.
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.
Ensuring the security and global compliance of your distributed systems can be difficult. In recent years, application security has become a paramount issue for both cloud-native enterprises and traditional businesses attempting digital transformation.
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.