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The saying that "data is the gold of the 21st century" has been encountered by almost everyone in recent years. But there is a crucial difference between data and gold. Data only have value when you use them. In order to enable usage, something has to change in many cases – the data have to be democratized.

Whe working with Tableau, sooer or later you stumble across the concept of Tablea sets. Explained next is what sets are and how they differ from groups. We'll also show you how create sets i Tableau in order to increase iteractivity.

In the Google cloud, Vertex AI is the MLOps framework. It is very flexible, and you can basically use any modelling framework you like. However, some frameworks are a bit easier to use than others: Tensorflow, XGBoost and Scikit-Learn are supported with some prebuilt images which are very helpful. This blog post will show how you can train and deploy models which are not generated by another framework. We will use a LightGBM model as an example, but the workflow can easily be transferred to any other modelling package.

As data scientists, we regularly train different machine-learning models in the cloud. Here you can find out how to structure your model training with the help of Python packages. Although each model has its own specific, intended application, some code snippets are ultimately copied from one project to another. In my case, this code is often for reading data from a database or for a pre-processing step. By allowing frequently used functions to be collected in one place, Python packages are ideal for avoiding this kind of code copying. This offers many advantages in the maintenance and testing of code.

In this blog article, we will see how a Python package can be utilized in GCP and integrated into a Vertex AI training job.

After taking a trip into the world of Ray in the first article, we now want to dedicate ourselves to Vertex AI – the key area of all machine learning services in GCP. Pipelines are meant to make life in the machine learning world easier. They promise to shorten development cycles through a high degree of automation. In addition, infrastructure abstraction is meant to allow teams to dispense with expertise in microservices etc. and instead focus on core competencies.

In this blog post, we will look at a simple example of how a machine-learning pipeline can be set up in Vertex AI.

SAP Data Warehouse Cloud offers new ways of processing and utilizing enterprise data. SAP BW Bridge for SAP Data Warehouse Cloud now makes it easier for customers with an on-premise SAP BW to proceed to the cloud. Here we show you what BW Bridge is all about, and how it supports your journey into the cloud!

It's been ten years since SQL Server 2012 was brought out. Ten years, and soon it will be over again – at least as far as extended support is concerned. Before we talk about what this means and what our options are, I would like to take this opportunity to look back on these ten years: On aspects whose foundations were laid back then, and which are now standard in the database landscape, especially in the BI context.

For specific use cases such as the contribution margin schema, it makes sense to display key figures in a dimension table instead of the classic fact table. This article describes characteristic features and a method of implementation in Power BI.

Increasingly frequently, Power BI is used not only as a self-service tool, but also forms the basis of operationalized analytics applications at many internationally active companies and even in the B2B sector. This, in particular, requires Power BI reports to be displayed in multiple languages. Here you have to decide: Do I create one report per language or try to make my report filterable by language? This article considers the latter approach.

This post deals in detail with the Protegrity platform and its interaction with Exasol. The Protegrity platform is meant to achieve extensive, end-to-end data protection over various applications and data storage devices.

Just as most of us prefer harmonic chords to dissonance in music, value creation is maximized only by harmonizing data strategy, data organization and data governance. We will address the most important issues and provide successful examples.

When we do simple descriptive data exploration, we are seldom content with analyzing mean values only. More often, we take a more detailed look at the distribution, have a look at histograms, quantiles, and the like. Mean values alone often lead to erroneous conclusions, and keep important information hidden. But if this is the case, why do we forget about this as soon as we build predictive models? These usually aim only at mean values - and they lie.