You can find tangible know-how, tips & tricks and the point of view of our experts here in our blog posts
An integrated Marketing Resource Management (MRM) solution is far more than just another tool in the system landscape. It becomes your central platform for managing and efficiently utilizing marketing resources, digital assets, budgets, campaigns, content, and approvals in one place. This significantly improves transparency, quality, and the ROI of your marketing activities. In this article, we outline different paths to successful integration and help you identify the best solution for your specific needs.
Power BI from Microsoft is a versatile, intuitive, and therefore widely used data visualization tool. But other software manufacturers are also making a name for themselves in the data evaluation market. One of them is Tableau. As an experienced Power BI developer, I was recently able to try my luck with Tableau for a customer. I would like to share the results with you in the following blog post.
Do you want to prepare a Power BI report, but the data quantity is too large to even start creating visuals? Or you've actually managed to prepare the report based on such data, but Power BI desktop keeps seizing up? Or the report takes forever to be published? You're not alone here. We will therefore next provide a few tips which are easy to implement.
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.
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.