In this webinar by Gerard Maas, senior engineer on Lightbend's Fast Data Platform team and co-author of the recent O’Reilly book Learning Spark Streaming, we walk through an introduction of both APIs. At the end of this presentation, you will have a bit more context for guiding your selection of the the right one for your application.

In conjunction with our partner O'Reilly, Lightbend is pleased to be able to offer you this expert guide to Machine Learning.

In this session by Konrad Malawski, author, speaker and Senior Akka Engineer at Lightbend, you will learn how to build these streaming ETL pipelines with Akka Streams, Alpakka and Apache Kafka, and why they matter to enterprises that are increasingly turning to streaming Fast Data applications.

As our partnership with O'Reilly grows, Lightbend is happy to present you with an exclusive preview edition of "Learning Spark Streaming", Chapters 1 and 2.

Back in May 2017, we launched our annual charity survey that looked into the rapidly evolving Fast Data, Streaming, and IoT ecosystem. We're thrilled that we received over 2400 responses—reaching our goal of $5000 donated to charity—as well as the opportunity to deeply examine the responses of thousands of developers regarding fast data adoption trends across three key areas—business value, use case, and technology.

This webinar by Emre Velipasaoglu, Principal Data Scientist at Lightbend, is for busy Architects and Managers looking to get a handle on what ML is really all about, the ideal use cases for ML and how getting it right can benefit your streaming and Fast Data application architectures. 

Watch this webinar with Dean Wampler, VP of Fast Data Engineering at Lightbend, Inc., to better equip you and your team with the context and background to make the right decision when it comes to adopting streaming frameworks.

In this webinar, Lightbend’s Senior Product Director, Craig Blitz reviews five early challenges enterprises face when building streaming and Fast Data systems, what to consider and how Lightbend aims to make these choices more straightforward with our upcoming Fast Data Platform.

It's not very often that you can make a big difference by doing very little...see how to convert 6 minutes into a meaningful donation to charity with latest survey!

This article takes a deeper look at how three traditional, brick-and-mortar style businesses–Norwegian Cruise Lines, Swisscom and UniCredit Group–crossed the chasm into Fast Data with technologies, support and services from Lightbend.

In this interview by O'Reilly Media, Lightbend's Duncan DeVore draws over 10 years of experience building Reactive systems to review the basics of what Fast Data is, how to address of design challenges for architects and developers, and discuss the importance of having a Reactive foundation for the next generation of streaming and Fast Data systems.

In this new white paper, we outline the functions a modern monitoring solution must perform in order to truly benefit from the advantages promised by Fast Data and streaming applications.

Companies are moving from systems designed for data "at rest" in data warehouses and embracing the value of data "in motion" at the edge–whether from user data, sensors or humans. Extracting information from incoming data as quickly as possible has become a competitive advantage. Stream processing is the best option and it has sparked new tools, frameworks, and architecture patterns for developers creating applications and systems designed specifically for real-time data. 

I sat down with Dr. Dean Wampler, Office of the CTO and Big Data Architect at Lightbend, we discuss how streaming data is shaking things up, and how the upcoming Lightbend Fast Data Platform is bringing Fast Data to enterprises looking to modernize today.

We are happy to announce the availability of Fast Data Architectures For Streaming Applications, a new O'Reilly report authored by the architect for fast data products at Lightbend, Dean Wampler (@deanwampler). In this report he examines the rise of streaming systems for handling time-sensitive problems —like detecting fraudulent financial activity as it happens. You’ll explore the characteristics of fast data architectures, along with several open source tools for implementing them.