In a survey of over 800 software professionals, we asked in which use cases real-time stream processing was most used, discovering that streaming is being embraced heavily for both traditional functionalities like ETL and monitoring, as well as new ones like ML/AI...

In a survey of over 800 software professionals, we asked in which use cases real-time stream processing was most used—and revealed an astonishing 500% increase in Machine Learning and Artificial Intelligence use cases in the last 2 years.

This post discusses stateful processing considerations when deciding whether your use case should be stateless or stateful when implementing stream processing.

This is the second of six posts in our cloud-native series that focuses on streaming data applications, exploring an alternative class of compute tools–libraries that provide streaming semantics–but which you embed in your microservices.

How, and why, do enterprises adopt streaming data applications in the cloud?

This is the first of six posts in our cloud-native series that focuses on streaming data applications and the requirements, architecture considerations, design patterns, and specific tools needed to do it right.

In this two-part blog series, we introduce the concepts and benefits of working with both spark-submit and the Kubernetes Operator for Spark. In Part 1, we introduce both tools and review how to get started monitoring and managing your Spark clusters on Kubernetes. In Part 2, we do a deeper dive into using Kubernetes Operator for Spark.

In the first part of this blog series, we introduced the usage of spark-submit with a Kubernetes backend, and the general ideas behind using the Kubernetes Operator for Spark. In this second part, we are going to take a deep dive in the most useful functionalities of the Operator, including the CLI tools and the webhook feature. At the end, we review the advantages and disadvantages of both spark-submit and Operator.

In a recent survey, 75% of over 2400 developers revealed that they are already using (or planning to use) microservices to enable real-time streaming use cases, showing that a key characteristic of Fast Data architectures is the use of microservices for streaming functionality...

In this webinar with Mike Kelland, VP of Global Services at Lightbend, we will share some details of our specialized enablement strategy that allows teams of all sizes to successfully adopt Fast Data technologies and techniques.

From real-time data streaming expert and VP of Fast Data Engineering at Lightbend, Dean Wampler, Ph.D., comes the 2nd Edition of his popular O'Reilly eBook Fast Data Architectures for Streaming Applications.

Two technologies–Akka Streams and Kafka Streams–have emerged as popular tools to use with Apache Kafka for addressing the shared requirements of availability, scalability, and resilience for both streaming microservices and Fast Data. So which one should you use for specific use cases?

You’re probably heard of the “SMACK Stack”, but you may not know that three Lightbend experts–Sean Glover, Stavros Kontopoulos, and Gerard Maas–that recently teamed up with O’Reilly Media on a free eBook about working with the SMACK stack for Fast Data applications (available in PDF, EPUB, and MOBI formats–or all 3 in a .zip folder)!

Machine Learning is one of the hottest topics in software engineering today. While it can deliver major competitive advantages, it’s also very challenging: creating the models is just the first step... 

In this webinar by Dr. Dean Wampler, VP of Fast Data Architecture at Lightbend, Inc., we cut through the buzz around Fast Data and explore how to successfully exploit this new opportunity for innovation in how your organization leverages data.

In our latest survey, Need For Speed: Fast Data Development Trends, over 2400 developers share their use of batch or streaming for data processing workloads...

In The Need For Speed survey, we asked over 2400 developers what’s hard about Fast Data through the lens of the complete project lifecycle: Design, Build, and Run.

Introducing Lightbend Reactive Architecture - Professional, a crash course in thinking differently, guided by experienced Lightbend expert who provides codified lessons learned, best practices, reference architectures, design patterns and analysis techniques when working with Reactive architectures.

Lightbend is happy to announce the release of version 1.1 of Fast Data Platform. Lightbend Fast Data Platform makes it easy for customers to develop, integrate, and operate a system comprising microservices and streaming data pipelines.

In this fireside chat style presentation, Dean Wampler of Lightbend and Matt Aslett of 451 Research will discuss the business justification for transitioning from batch-oriented big data to stream-oriented fast data, and how some of these requirements can be met by leveraging what organizations already know about microservice architectures.