This is an excerpt from a draft of chapter 10 of my book, Azure Data Engineering, which deals with compliance. In this article we’ll look at a few techniques to transform sensitive user data into less sensitive data. In the book, this includes code samples for implementation on Azure Data Explorer, which are omitted from this article. Let’s start with a couple of definitions.
From Operations Anti-Patterns, DevOps Solutions by Jeffery D. Smith
From Cloud Native Spring in Action by Thomas Vitale
From Operations Anti-Patterns, DevOps Solutions by Jeffery Smith
This article covers
Longer release cycles and their impact to the team’s deployment confidence
Automation techniques for deployments
The value of code deployment artifacts
Feature flags for releasing incomplete code
From Designing Cloud Data Platforms by Danil Zburivsky and Lynda Partner
In this article, we’ll layer some of the critical and more advanced functionality needed for most data platforms today. Without this added layer of sophistication your data platform would work but it wouldn’t scale easily, nor would it meet the growing data velocity challenges. It would also be limited in terms of the types of data consumers (people and systems who consume the data from the platform) it supports, as they’re also growing in both numbers and variety.
From Making Sense of Edge Computing by Cody Bumgardner
Conceptually, edge computing is concerned with when it’s best to migrate computational functionally toward source of data and when it is best to move the data itself. This abstract concept of function versus data migration drives not only the fundamental motivations of edge computing, but also the broader field of distributed systems. The act of distributing processes makes even the simplest tasks more complicated.