March 19, 2019

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

In this webinar with Lightbend's Craig Blitz, Product Director, and Kiki Carter, Principal Enterprise Architect, we review how Lightbend’s Pipelines module enables you to develop components ("streamlets") using the appropriate technology, wire them together as pipelines, and deploy them with Kubernetes without all the manual, time-consuming labor.

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Chaoran Yu

Senior Engineer

Stavros Kontopoulos

Senior Engineer
February 26, 2019

How To Manage And Monitor Apache Spark On Kubernetes - Part 1: Spark-Submit VS Kubernetes Operator

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.

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Chaoran Yu

Senior Engineer

Stavros Kontopoulos

Senior Engineer
February 26, 2019

How To Manage And Monitor Apache Spark On Kubernetes - Part 2: Deep Dive On 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.

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Oliver White

Chief Storyteller
February 22, 2019

A Glimpse At The Future Of Apache Spark 3.0 With Deep Learning And Kubernetes

In this special guest webinar with Holden Karau, speaker, author and Developer Advocate at Google, we’ll take a walk through some of the interesting Spark 3.0 JIRA tickets, look at external components being developed (like deep learning support), and also talk about the future of running real-time Spark workloads on Kubernetes.

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September 14, 2018

Free O'Reilly eBook: "Designing Fast Data Application Architectures" With The SMACK Stack

You’re probably heard of the “SMACK Stack”, but you may not know that three Lightbend experts–Sean GloverStavros 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)!

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October 13, 2017

Design Streaming Fast Data Applications with Spark, Akka, Kafka and Cassandra on Mesos & DC/OS

In this webinar with Craig Pottinger, Senior Consultant at Lightbend, we examine the design choices around building streaming systems with technologies like Akka Streams, Apache Kafka, Apache Spark, Apache Flink, Mesosphere DC/OS and Lightbend Reactive Platform, all of which come integrated with Lightbend Fast Data Platform.

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April 3, 2017

How To Get Monitoring Right For Streaming And Fast Data Systems Built With Spark, Mesos, Akka, Cassandra and Kafka

In this Lightbend webinar, we present what to look for in an effective monitoring solution for streaming and Fast Data applications, including a demo of Lightbend Monitoring using sample scenarios to show how we can help your team not just in production, but also during development to catch performance issues before you even deploy.

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March 13, 2017

Building Streaming And Fast Data Applications With Spark, Mesos, Akka, Cassandra And Kafka

In this webinar with Sean Glover, Senior Consultant at Lightbend and industry veteran, we examine the rise of streaming systems built around Spark, Mesos, Akka, Cassandra and Kafka, their role in handling endless streams of data to gain real-time insights, and how Lightbend Fast Data Platform (FDP) brings them together in a comprehensive, easy to use, integrated platform, which includes installation, integration, and monitoring tools tuned for various deployment scenarios, plus sample applications.

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September 20, 2016

Fast Data for Telecommunications: Swisscom Q/A On Choosing Scala And Spark For New Streaming Data Platform

Recently, we published a case study with our customer Swisscom, the leading mobile service provider in Switzerland. While the case study focuses more on the solution and implementation details of how Swisscom used Scala and Spark to build a fast data streaming platform from the ground up in just 9 months, there is more to the story. To get a fuller picture, we sat down with Francois Garillot, Big Data Scientist at Swisscom...

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March 7, 2016

Lightbend Training for Scala, Akka, Play and Apache Spark

Having a team adopt new technologies and approaches to software development is a daunting task.  New paradigms and unfamiliar ontologies headline the biggest risks to having a team be productive quickly.  Lightbend has a suite of training classes to help you adopt whatever components of Reactive Platform you need to be responsive to you customers by creating resilient and elastic applications.

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February 21, 2016

The How and Why of Fast Data Analytics with Apache Spark

In recent years, Fast Data, not Big Data, has been more important to react to real-time events and decision making; slower batch processing of large data sets with Hadoop is not always as important for most SMEs. So if your data pipeline is bloated and ready for an upgrade, this webinar will provide you with an overview of what Spark is and gain an understanding of why it is indeed the right tool to improve your Fast Data strategy.

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January 12, 2016

Fast Forward With Fast Data, Scala and Akka: Q/A with Spark Job Server creator

People love Apache Spark. Typesafe is the official support partner with Databricks, Mesosphere and IBM for Apache Spark. If you’re a Fast Data visionary, then you’re looking for a modern solution to enable your streaming data applications. In today’s DevOps world, you’re looking for a more accessible way to work in particular with Spark Jobs. Instead of directly mingling around with the specific Spark installation, you want to have access to a lot of functions via lightweight protocols remotely. This is the gap that Spark Job Server closes.

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December 1, 2015

Typesafe Reactive Platform: Monitoring 1.0, Commercial features and more

Were you able to attend our latest webinar? If not, here's your chance to catch our educational go-to guy get excited about the lastest in our Reactive Platform. Typesafe’s Senior Director of Global Services, Jamie Allen, walks you through Reactive Platform 1.0, with added features for more elasticity and resilience to address the challenges of today's main business requirements.

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November 4, 2015

Rocking out at Datapalooza with Cake: Interview with Jan Machacek

If there’s one thing that’s clear this fall, it’s that conference season is in full swing. With Typesafers attending JavaOne in San Francisco, Spark Summit in Amsterdam, W-JAX in Munich, Devoxx in Belgium, YOW in Australia, Gartner Summit in Las Vegas, Scala eXchange in London and more popping on our calendars by the minute, our dance card is pretty full. That said, sometimes certain events come up that just catch your eye and you must make time for them.

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October 9, 2015

How to deploy Spark to Mesos, EC2 or standalone with Typesafe

Let's talk about Spark. In this fantastic webinar, Iulian Dragos, Spark team lead at Typesafe, shows how Typesafe supports running Spark in various deployment modes. He also discusses the improvements we recently made to Spark to help integrate backpressure signals into the underlying technologies, making it a better (dare we say AWESOME?) fit for Reactive Streams. He'll show you how to deploy Spark in various deployment modes: Standalone, Spark on Mesos, and Spark with Mesosphere’s Datacenter Operating Systems (DCOS). This last option is especially helpful for our customers in need of support. Typsesafe is proud to be the official commercial support provider of Spark on Apache Mesos, along with DCOS.

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July 14, 2015

Four Things to Know about Reliable Spark Streaming with Typesafe and Databricks

Last week, we were happy to have a Typesafe co-webinar with Databricks, the company founded by the creators of Apache Spark. Our Big Data Architect Dean Wampler and Datatbrick's Lead Engineer for Spark Streaming, Tathagata Das (TD) provided a 1-hour presentation with Q/A on Spark Streaming, which makes it easy to build scalable fault-tolerant streaming applications with Apache Spark.

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June 23, 2015

Using Spark, Kafka, Cassandra and Akka on Mesos for Real-Time Personalization

The gambling industry has arguably been one of the most comprehensively affected by the internet revolution, and if an organization such as William Hill hadn't adapted successfully it would have disappeared. Watch Patrick Di Loreto, R&D Engineering lead for William Hill, deliver to nearly 1300 registrants what his company is doing to grow in a fast-paced industry where milliseconds can matter to users and real-time data analysis and reactions are the keys to competitive advantage. 

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March 5, 2015

Eight hot technologies that were built in Scala

scala

With Scala Days 2015 San Francisco just around the corner (and only 15% of tickets left), it has got me thinking quite a bit about how much the ecosystem has expanded since I first became involved with the conference in 2011. 

The rapidly-growing Scala community has evolved from what was largely a very academic and research-oriented crew, with some early champions like Twitter and Foursquare, to a language that’s become a standard for enterprises, start-ups and universities alike. 

But even as companies and individuals use Scala to build their own new ideas, they also utilize other excellent tools like Play Framework, Akka, Apache Spark and Kafka...which are not only some of the hottest tools and projects on the market right now, but also intentionally built in Scala (for many reasons…)

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December 3, 2014

Spark Survey

Back in September, we ran a survey to gather people’s thoughts and upgrade plans around Java 8. We were surprised to find that among the 3,000 respondents, more than 17% are already using Apache Spark in production. Considering how Spark support by the major Hadoop vendors is only about a year old, this number took many by surprise.

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