In this post we take a look at how to quickly create a Python function using AWS Lambda, including their configurations and uploading them to the service.
Learn about dissecting release pipelines, as well as the underlying blocks that any good release pipeline will be comprised of and how they fit together.
You can augment and enhance Apache Spark clusters using Amazon EC2's computing resources. Find out how to set up clusters and run master and slave daemons on one node.
When it comes to integrating and managing data, there are quite a few tasks that are downright tedious. Data engineering is a tough job, but somebody's gotta do it!
Set up your pipeline so that when changes occur to a project in the monorepo, the CI for that corresponding project is triggered and a Docker image is built and deployed.
Running tests that involve a database can sometimes be a bit hairy. In this post, we take a look at a TestContainers, a solution to help you get around those issues.
You've got three ways. The first one is the standard one following docker-compose conventions. The other ones can be used for defining reusable pieces for your tests.
We link Docker containers with each other to enable communication between them or to be sure that all of the tools and microservices are running on the same machine.
If you’re using Docker, the combination of Prometheus and Grafana offers an extremely enticing option to explore for reasons of ease of use and functionality.
CD is a natural evolution from CI and Agile software development practices. However, the cultural and operational challenges to achieving it are much greater.
Clustering and high availability configuration with RabbitMQ are pretty simple. Its UI management console offers good s support in the cluster monitoring process.
I'm positively surprised by Apache Camel. Before I started working on this example, I didn’t expect it to have so many features for microservice solutions.
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