The latest News and Information on Cloud monitoring, security and related technologies.
One of the benefits of Serverless architectures is the possibility of scaling applications without worrying about load balancers and clusters of servers. While services like AWS Lambda hold their promises on this area, there are usually misconceptions about how they work. It is common for developers to assume that Lambda functions can scale infinitely, at any speed, in any circumstances. Reality is quite different from that.
Organizations around the world are increasingly relying on the cloud to capitalize on its speed, ease of management and scalability, and the business value it provides to transform and grow their business. It’s an ever-growing market that is currently estimated at 266.4 billion dollars—a whopping 982.9 percent increase in growth compared to a decade ago when it was worth a little over 24.6 billion dollars.
Chaos Engineering was conceived as a direct response to the complexity and nondeterministic nature of cloud-based applications. Thoughtful fault injection closes the gap between traditional testing methodologies and modern approaches to software engineering like microservices, continuous delivery, and DevOps.
Today, the AWS Lambda platform has added a new arrow to its quiver – the ability to integrate with Amazon Elastic File System (EFS) natively. Until now, a Lambda function was limited to 512MB of /tmp directory storage. While this is sufficient for most use cases, it’s often prohibitive for use cases such as Machine Learning, as Tensorflow models are often GBs in size and cannot fit into the limited /tmp storage.
Serverless computing has taken off in recent years as engineering organizations have shied away from the complexity and cost that comes with managing physical servers and even virtual machines hosted on a cloud like AWS. If you are interested in serverless options on AWS, you should be comparing Lambda and ECS Fargate to figure out what fits your use case best. In this blog post we will compare these two AWS services in the following areas...
There are many challenges that engineering teams face when attempting to incorporate a multi-cloud approach into their infrastructure goals. Kubernetes does a good job of addressing some of these issues, but managing the communication of clusters that span multiple cloud providers in multiple regions can become a daunting task for teams. Often this requires complex VPNs and special firewall rules to multi-cloud cluster communication.