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 with more efficient cloud services. AWS Lambda is a great example of this, where provisioning and management of resources happens from the service’s end. You only have to deal with the code. This article will give a brief overview of AWS Lambda in contrast to EC2 instances, then walk through shipping AWS Lambda metrics to Logz.io.
Lambda is an AWS service that allows you to run your code completely in the cloud without the need for on-prem servers. As the world has shifted away from on-prem and toward serverless computing, Lambda has grown in popularity: 1700 companies report using Lambda as part of their data infrastructure in StackShare.
Technology touches almost every corner of the world economy. Even when it’s an indirect relation, in many cases tech is an essential, vital part of our societies. It just can’t fail without causing too much distress and losses. Not only financially, but especially to the human aspect.
As we all know, the on-demand capacity mode of DynamoDB is great but can be cost-prohibitive in some cases (up to seven times more expensive than the Provisioned Capacity mode). The Provisioned mode, on the other hand, shifts to the development team the burden of predicting what level of capacity will be required by the application. And it’s not quite as straightforward to achieve the same level of scalability in the Provisioned mode as we enjoy in the On-demand one.
This post was originally published in JAXenter.com. Serverless is a relatively novel concept and cloud architectural model but has been advancing very quickly over the past 5 years. In this article, we’ve compiled a list of recent changes that are likely to shape how development teams use Serverless in practice. In this article, we’ll be heavily focussing on AWS serverless services. The cloud provider has been investing heavily in the advancement of Serverless.
Data ingestion and data analysis are the yin and yang of a time series platform. There are many resources to help you ingest data. Typical ingestions are agent-based, imports via CSVs, using client libraries, or via third-party technologies. Once your time series data arrives, analysis completes the circle and often leads to additional data collection, and so on and so forth.
