Cloud Transformation & Data Infrastructure

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.

I had just arrived at b.telligent with a PhD in pure mathematics and a mixed bag of programming and IT skills in my pocket. My goal: to become a certified Azure Architect within 4 months. The learning pathway: a professional development program from Microsoft and a lot of support from b.telligent.

I am not writing this blog post in a quiet minute in our b.telligent offices, but live from the Spark Summit in Brussels. For data scientists, it offers an enormous scope of machine learning procedures, both traditional for static data sets, and for streaming data in real-time. All those with practical experiences in the Python library sklearn will immediately feel at home, as this served as an example.