Take a look at our most read and shared blog posts from March 2021.
Last week, the official Python Package Index website faced a large-scale automated attack where more than 3500 malicious packages were added to the index, aiming to be downloaded by unsuspecting developers. This article will explain the approach and goal of this campaign called a supply chain attack, and why such attacks are extremely powerful. But first, a little bit of context.
Anti-cheat software is part of an endless cat-and-mouse game: developers find a way to detect and punish cheaters, who in turn try to evade detection by building better cheats. This cycle can only be halted momentarily, when either developers or cheaters manage to out-smart the other. A few years ago, a game editor managed to do exactly that, in a brilliant way.
Leading… so many literature is published every day on this topic. Let’s replace our management books in their cozy library and compulse 7 tips to effectively lead a team.
Just as mankind has evolved over the centuries, our means of communication have changed as well. What began as primitive cave paintings and signed language has now transformed into endless of varieties to express oneself. To cope with the increasing complexity our clients are facing, effective communication is needed. After all it enables us to pass, and understand, information more accurately and quickly. Visual thinking, which is a set of methods used for describing words as a series of pictures, is one way to achieve that.
In this article we will describe in detail the steps to follow to complete the automatic deployment of Azure Data Factory pipelines in the Development (dev), Staging (stg) and Production (prd) environments. In software development, the use of integration (CI) and continuous deployment (CD) is done to release better code in a fast way. This possibility also exists for data engineers working with Azure Data Factory. That is, we will have the possibility of moving pipelines between the different environments. Furthermore, working in this way, several people in the team may be working at the same time on the same data pipeline. In this case, we are going to work on an example of automating the deployment from dev to prd. All thanks to ADF integration with Azure DevOps Automation. Let’s see how.