To keep ahead of the curve, many organizations are looking at how to evolve their technical processes to accelerate their IT infrastructure development. Fast and robust deployments to the latest platforms are key to achieving the low lead times that enable this evolution. Two of the most widely-used technologies to host these deployments are serverless functions and containers. What are they, how do they differ, and how do you decide which is best for your application?

Lumigo uses IntelliJ IDEs everywhere. The back-end developers love their PyCharm and us frontend developers use WebStorm all the time. No doubt that it’s one of the most popular IDE’s out there. One of the perks at Lumigo is that as employees, we can use 10% of our working time to invest in personal projects or do cool things for self-development and innovation.

Microservices are a popular architectural style for building applications that are resilient, highly scalable, independently deployable, and able to evolve quickly. But a successful microservices architecture requires a different approach to designing and building applications. A microservices architecture consists of a collection of small, autonomous services. Each service is self-contained and should implement a single business capability within a bounded context.

Serverless is a technique for executing operations and running cloud compute services on an as-needed basis. Serverless computing is the latest trend in the cloud computing world. It has made it much easier to develop, deploy, and scale applications. Serverless computing means that developers don't need to worry about anything other than their code. They don't need to provision a server or install software to run their code.

AWS Lambda execution lifecycle has main phases: initialization, invocation, and shutdown. In the initialization phase, Lambda creates the runtime environment, downloads the code, imports everything needed, and runs the functions initialization code. In the invocation phase, the Lambda will get an input, process it, and produce an output. After the invocation phase, Lambda will go to an ideal state and wait for the next input.

Our powerful solution provides visibility into your workloads running in AWS Lambda.

Adoption of AWS Lambda functions in cloud-native applications has increased exponentially over the past few years. Serverless functions, such as the AWS Lambda service, provide a high level of abstraction from the underlying infrastructure and orchestration, given these tasks are managed by the cloud provider. Software development teams can then focus on the implementation of business and application logic.

Serverless360 serves as an all-in-one add platform solution to manage and monitor Azure Serverless Applications. A massive set of product documentation enables a DevOps Engineer, Azure Developer, or Support Engineer to understand and appreciate how Serverless360 can improve their Azure experience. Even if the DevOps Engineer, Azure Developer, or Support Engineer understands the value of Serverless360, they must persuade management to purchase the product.

Microsoft Azure is a fantastic platform that allows customers to access many cloud resources that customers can then connect to solve business problems. There is enormous power in the platform, and it’s like having your giant box of Lego bricks, which you can build into any solution. The challenge with a platform in which you can build anything you want is that when your application is built, the platform view makes it difficult to create a way for your non-cloud experts to support the end solution.

Gone are days of large applications having tens of servers to deal with gigabytes of data, when seconds of response time and hours of offline maintenance were acceptable. Modern applications are deployed on everything running thousands of multi-core processors; end-users expect millisecond response times and 100% uptime. Need not mention the applications work with Data in Petabytes.