Large-scale Data Analytics with Azure Synapse - Workspaces with CLI
One of the challenges of large scale data analysis is being able to get the value from data with least effort. Doing that often involves multiple stages: provisioning infrastructure, accessing or moving data, transforming or filtering data, analyzing and learning from data, automating the data pipelines, connecting with other services that provide input or consume the output data, and more. There are quite a few tools available to solve these questions, but it's usually difficult to have them all in one place and easily connected. This article will cover what Azure Synapse is and how to start using it with Azure CLI.
Application state – I want the easiest, most trouble-free deployment for the database
As more and more applications convert to cloud native with Kubernetes as a normalizing layer, the ability to have portability between public clouds also becomes easier. Theoretically, an application can now move easily between Clouds. However, there are a couple of questions that you still have to consider: Should I connect to cloud services such as ML/AI, DevOps tool chains, DBs, Storage, etc.? This might lock me in. Should I create my own PostgreSQL database, manage and scale it? What is a great solution on the Cloud for PostgreSQL? We'll examine this in the blog and talk about Hyperscale (Citus) on Azure Database for PostgreSQL as a possible solution.
