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In the ever-evolving world of AgileData DataOps, it was time to upgrade the Python version that powers the AgileData Platform.
We utilise micro-services patterns throughout the AgileData Platform and a bunch of Google Cloud Services. The upgrade could have gone well, or caused no end of problems.
Read more on our exciting plumbing journey.
TD:LR Cloud Data Platforms promise you the magic of storing your data and unlimited elastic compute for cents. Is it too good to be true? Yes AND No. You can run a cloud platform for a low low cost, but its will take...
We wanted to be able to dynamically notify Data Magicians when a task had completed, without them having to refresh their browser screen constantly. Implementing websockets allowed us to achieve this.
TD:LR … you don’t always need to use DAGs to orchestrate Previously we talked about how we use an ephemeral Serverless architecture based on Google Cloud Functions and Google PubSub Messaging to run our customer data...
TD:LR When we dreamed up AgileData and started white-boarding ideas around architecture, one of the patterns we were adamant that we would leverage, would be Serverless. This posts explains why we were adamant and what...
Pick a few things that really matter, not thousands of “requirements” We when first started developing the core of the AgileData backend for the MVP, we knew we would need a cloud database to store...
As part of our journey to exploring the use of the Google Cloud Platform (GCP) as the core of our infrastructure for AgileData.io we have had to start unlearning a lot of our AWS and Microsoft Azure knowledge and...
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