Cloud Transformation & Data Infrastructure

You want to manage your infrastructure with Terraform, but then it happens – manual changes are made, and you need to find a solution. How to handle this depends on the specific case.

One of Terraform’s greatest strengths is its ability to handle changes made outside its managed resources. The keywords are: data, import, removed, ignore_changes, lock, variables.

Many security considerations involving Azure revolve primarily around network security. Other important security aspects to be considered in the context of Microsoft Fabric are indicated below.

How can I integrate data sources that are secured via private endpoints into Fabric? How do I deal with Azure Data Lakes behind a firewall? This blog post shows the possibilities which Fabric Nativ offers

As stated in part one of this blog series on the reference architecture for our cloud data platform, we will share and describe different parts of this model, and then translate it for the three major cloud providers – Google Cloud Platform, AWS, and Azure. In case you just came across this blog post before seeing the first one about the ingestion part of our model, you can still read it here first. For all others, we’ll start by looking at the data lake part of the b.telligent reference architecture before diving deeper into analytics and DWH in part 3.

The world of medium-sized companies is being stirred at present by one topic, in particular: Industry 4.0. Pressure here is growing enormously because the digitization bus must not be missed if successful interaction on the market is to remain possible in the long term. What most companies do not know, however: Not all internal procedures must be cast overboard in order to remain abreast of the issues of big data and Industry 4.0. It is much more important make use of one's own employees' curiosity and convey the data culture to all departments. This blog post shows how to master the change to digitization step by step.

Ever thought about what the architecture of a cloud data platform should look like? We did! In our free webinar series Data Firework Days, we introduced our b.telligent reference architecture for a cloud data platform, a blueprint of how to build a successful data platform for your analytics, AI/ML, or DWH use cases. And we went a step further. Since we all know there’s not just one cloud out there, we also translated our model for the three major cloud providers – Google Cloud Platform, AWS, and Azure. In this blog series, we intend to describe the reference architecture in the first three blog posts and then, in parts 4–6, we’ll look into implementation options for each of them. So, do join us on our journey through the cloud.

Congratulations, you’ve managed to get through previous sections of our reference architecture model unscarred! The most tedious and cumbersome part is behind us now. However, it’s no problem if you're just getting started with part 3 of our blog series! Simply click on the links to part 1 and part 2, where we take a closer look on ingestions and data lakes as well as the entire reference architecture.

Continue with part 1 and part 2 of this blogseries.

Architecture recommendations and data-processing techniques with Azure Synapse Analytics. This article of ours provides two architecture recommendations, besides showing how they ca be implemented an how data are provided for visualization.