Keeping up with the pace of change in emerging technology is an ever present challenge for enterprises. Trying to build systems that age and evolve gracefully over time is not a small task. We are all familiar with frameworks that help us implement architectural patterns, especially application architecture. In this session we will investigate Lagom, … Read More
This is a continuation of my Internet of Things with Scala posting. In this installment I will go over the assembly of the device that will be used to capture the soil moisture data and send it to an MQTT broker so that it may be picked up by the Scala/Akka based application. The Parts … Read More
There has been a great deal of buzz around the Internet of Things lately. The advent of small inexpensive devices and in particular the Arduino has inspired a generation of people with no background in electrical engineering to do some very creative things. I myself was first inspired by the Arduino and had built several … Read More
I had recently come off of a three year contract and found myself with some time on my hands. I was interested in working on an application that interfaced directly with some sort of small device. The director of our company, Don Coleman, had done a good bit of work with small devices so I spoke to him about the possibility of building a small application around those devices. He agreed to it and offered to assist from the hardware end. Our director of training, Ken Rimple, was researching the Angular 2 JavaScript framework at the time and offered to build the UI for the application. We eventually decided that we would build a demo to display at our booth at the 2016 South by SouthWest conference. This post explains our approach and some lessons learned.
In today’s world of exploding big and fast data, developers who want both streaming analytics and ad hoc, OLAP-like analysis have often had to develop complex architectures such as Lambda—a path for fast streaming analytics using NoSQL stores such as Cassandra and HBase with a separate batch path involving HDFS and Parquet. While this approach works, it involves too many moving parts, too many technologies for ops, and too many engineering hours. Helena Edelson and Evan Chan highlight a much simpler approach to combine streaming and ad hoc/batch analysis using what they call the NoLambda stack (Apache Spark/Scala, Mesos, Akka, Cassandra, Kafka), plus FiloDB, a new entrant to the distributed-database world that combines streaming and ad hoc analytics.
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On today’s TechCast, Sujan Kapadia talks to yet another Scala person: James Roper (@jroper). Previously a core team member of the Play framework, James is now the tech lead of Lagom, a new Java-oriented micro-services framework introduced by Lightbend. He discusses it in depth in his ETE talk, Rethinking REST in a Microservices World, as well as in this conversation with Sujan.
In today’s world of exploding big and fast data, developers who want both streaming analytics and ad hoc, OLAP-like analysis have often had to develop complex architectures such as Lambda—a path for fast streaming analytics using NoSQL stores such as Cassandra and HBase with a separate batch path involving HDFS and Parquet. While this approach works, it involves too many moving parts, too many technologies for ops, and too many engineering hours. Helena Edelson and Evan Chan highlight a much simpler approach to combine streaming and ad hoc/batch analysis using what they call the NoLambda stack (Apache Spark/Scala, Mesos, Akka, Cassandra, Kafka), plus FiloDB, a new entrant to the distributed-database world that combines streaming and ad hoc analytics.
This webinar will describe a reference architecture using the Typesafe Reactive Platform and other tools such as Cassandra, Kafka, and Spark that can be used to build out the real-time organization.
This talk will explain the rationale behind CQRS/ES, how it fits the Reactive paradigm and the advantages regarding both the technology and business level.
https://youtu.be/6RQb7Hz_TPw Setting up Akka clustering is quite easy and provides a way to distribute work across multiple machines in the cluster. In many cases this requires no changes to an Actor compared to a non-cluster deployment. In this screencast, I walk through setting up a cluster and compare deploying an actor in a local actor … Read More
