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We recently explained how you can read out machine data with an edge device, visualize it in Azure, and prepare it for further processing. This post looks at the same question – only in AWS.

Ray enjoys a growing popularity in the machine learning community. Getting it up and running under Windows can be tricky however. This blog tells you how.

By deciding in favor of the SAP Analytics Cloud (SAC), you are choosing an innovative, flexible and high-performance cloud solution for your company. The next step is to choose the right analysis and visualization method for your use case. Here, we'll give you an insight into the SAC develoopment environments.

In our free series of online events under the banner of Data Firework Days, we introduced you to the b.telligent reference architecture for cloud data platforms. Now we'd like to use this blog series to take a closer look at the subject of the cloud and the individual providers of cloud services. In the first of this three-part series Blueprint: Cloud Data Platform Architecture, we were interested in the architecture of cloud platforms in general.

Read part 1 here: Blueprint: Cloud Data Platform Architecture

Exasol is a leading manufacturer of analytical database systems. Its core product is a high-performance, in-memory, parallel processing software specifically designed for the rapid analysis of data. It normally processes SQL statements sequentially in an SQL script. But how can you execute several statements simultaneously? Using the simple script contained in this blog post, we show you how.

Have you ever stumbled across the following problem? Your database contains a table of versions, and you happen to notice there are almost no relevant changes from one version to the next, which means you have way too many rows. Let’s show you how to easily solve this problem.

Long waiting periods are an irritation and a cause of much frustration when working with Tableau. In this case study, we look at how to get the performance of your Tableau data source back under control - even if you're using a live connection or a complex data model.

If your company is ever to master the challenges of the Internet of Things, the one thing you must know is your IoT maturity level. Does your company have a large number of connected devices and valuable hidden data? Have you already successfully implemented IoT data analytics to create value from your processes? Do you have sufficient expertise in data storage and the management of high-performance cloud databases? All of these questions influence how you should best approach the Internet of Things.

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.

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.

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.

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.