AWS Lambda features rev Node.js support
Breathe easier, serverless application developers — your lengthy wait is over, with AWS Lambda’s added support for a newer Node.js runtime
After a year’s wait, AWS developers can now use Node.js version 8.10 to enable a number of AWS Lambda features that were on their wish lists. The async/await pattern makes it much easier to implement asynchronous calls without muddying up the code with callbacks or promises, which make it difficult to read. The support update also simplifies error handling, which further reduces unnecessary code, and it offers faster runtime and render time speeds.
In the past, AWS has been slow to add Lambda support for other languages as well, including Python, though Amazon’s lengthy code review process, which ensures no potentially damaging code exists in releases, are a big reason for that delay.
Meanwhile, a pair of new security tools unveiled at AWS’ yearly San Francisco Summit in early April.
The AWS Secrets Manager service enables an administrator to abstract the manual process to store, manage and retrieve encryption keys, database credentials and other secrets. The service saves time and cost to stand up infrastructure to specifically manage secrets, a process complicated by increasingly distributed applications. Secret Manager also enables you to rotate credentials with a Lambda function.
With the AWS Firewall Manager, admins can define and apply Amazon Web Application Firewall security rules across various cloud applications and accounts. The service centralizes security management, which enables grouped control and enhances visibility of attacks on Application Load Balancers and CloudFront workloads, to help enterprises adhere to compliance requirements.
Living on the edge
Two AWS offerings became general available in April to help enterprises more quickly process IoT data, in different ways.
AWS IoT Analytics enables users to process raw data directly from IoT devices and sensors. For some enterprises, however, the cost of data transfers is prohibitive, so it’s appealing to preprocess data before it reaches the cloud. The AWS Greengrass ML Inference an enterprise can deploy cloud-trained machine learning models on connected devices for locally collected data. Combined, these two offerings enable real-time data processing at the edge and more detailed analytics when chosen data reaches the cloud.
One other service update doesn’t open up new AWS Lambda features for serverless developers, but it does open up the code base and removes a barrier to automation.
AWS open-sourced its Serverless Application Model (SAM) implementation, with which developers define resources spun up by CloudFormation stacks. Previously, developers submitted feature requests to AWS which would change the implementation. With an open-source SAM implementation, developers can more quickly specify new features and enhancements, and then build serverless apps.