With your digital transformation, a tremendous amount of development lays ahead. If your developers are hamstrung by old tools, it will be nearly impossible to keep your project on track and your top talent engaged.
Because the actor-model is at the heart of our application development platform, any developer in any size of enterprise can build highly distributed applications that react user demand, react to web-scale load, and react to inevitable failure—all by design.
Industry analyst Forrester reports a much larger proportion of the developer population finds the actor model approachable compared to writing multithreaded code within object-oriented frameworks. The actor model provides a higher level of abstraction for writing concurrent and distributed systems. It alleviates the developer from having to deal with explicit locking and thread management, making it easier to write correct concurrent, parallel and distributed systems.
Additionally, our expert engineers have developed the tools your developers—and operations—need to efficiently deploy and manage distributed systems including monitoring, self-healing, and service orchestration. Rather than building out low-level plumbing or reinventing the wheel, your team can focus their efforts where they can have the highest impact on your business.
Increasing the speed of new feature creation and overall developer velocity are key business drivers for iHeartRadio. In a competitive landscape where users flock to the best methods for discovering and interacting with music, the rate of feature innovation is a key business driver.
Previously the legacy monolithic application presented so many dependencies and such a massive code base that it was very hard for iHeartRadio to bring any new team members up to speed. With today’s microservices approach, each service is one to several processes at a maximum, with less than 1000 lines of code that are easy to reason with, which has made a huge difference in terms of onboarding new developers.Case Study
To better understand the needs of future customers, UniCredit’s team was tasked with easily accessing and rapidly analyzing decades of historical data. They started off by implementing Cloudera’s Hadoop distribution and HBase, namely as a way to bring enormous quantities of disparate data into one place. But when it came down to bringing this data into motion and making it valuable through algorithmic, graphical data analysis, this solution was insufficient.
Lightbend proved to be an easy and effective way to deal with distributed data computation for complex processing pipelines, where a “microservice-style” approach to computation was efficiently crunched by clusters of actors. Within a matter of weeks, the UniCredit team was able to deliver a highly-performant data pipeline prototype to management that was resource efficient and resilient, and also fun to work with.Case Study