In the heart of amazing software development, in the city Brno, we launched a blog to share our thoughts.
E-commerce platforms like Shopify contain their reporting dashboards which cover tons of figures generated by the shop, but it might not be sufficient for all architectures. We looked at the case when a customer wants to achieve real time analytics scaling seamlessly for peak periods like Black Friday.
The MGS team is now introducing a connector between MGS and Databricks. Teams governing the firm’s portfolio in MGS - definitely not only ML/AI models - get access to the details of model development and monitoring right at their fingertips. Teams using the Databricks platform can write code interacting with the inventory.
Can we use the Databricks as a runtime platform for complex applications using the standard development model with git, deployment pipelines and several independent modules / services?
The software during the development cycle will go through many stages. It usually starts as a source code in IDE, different pars will be executed in unit tests, maybe even whole components will be tested. It’s somehow packaged (compiled) and deployed to some server (staging, production,…). That is a very rough lifecycle which is not applicable in all cases, but at least during my time in CloseIT, many projects followed it.
When I started putting together my first AWS SAM project, I was confused with the project structure - as always, when I’m starting new project with new technology. You can easily make a bloated project where code is duplicated in each lambda function.