The latest News and Information on Cloud monitoring, security and related technologies.
Migrating your on-prem applications to Azure can help you improve scalability, reliability, and security. It can also help reduce costs and free your engineering teams to focus on innovation and performance optimization. But it can be hard to understand Azure costs as they evolve during your migration and to see how they correlate with your resource utilization once you’re up and running in Azure.
This is the third and final post (for now) in the series about developing email templates with MJML and deploying them to AWS. In the previous post, we developed a Gulp script to automatically build HTML from the MJML file and insert it in a template file for AWS. In this post, we will set up an automated build and deployment of the email template using Azure DevOps. A quick recap.
Azure Blob Storage is a scalable, cost-effective, and durable cloud storage solution provided by Microsoft Azure. Serving as the backbone for many Azure services, it enables businesses to store a colossal amount of unstructured data ranging from documents, images, backup data, to log files, etc. Azure Blob Storage can handle all your static data that’s stored and read but not changed frequently, making it an indispensable part of any cloud data management strategy.
At Lumigo, we heavily depend on a set of tests to deploy code changes fast. For every pull request opened, we bootstrap our whole application backend and run a set of async parallel checks mimicking users’ use cases. We call them integration tests. These integration tests are how we ensure: Recently, we changed our old “traditional log traversing” of integration tests into *amazing* OpenTelemetry traces graphs.