Blog

Currently, a popular component of Cloud Data Platform Architectures is a Data Lake. If you are curious about the implementation and services for a Data Lake with AWS, have a look at those blogposts. An architecture which provides transparency about the Data in the Data Lake and makes it smoothly available for further analytics in a Data Warehouse, is called a Data Lakehouse. Click here.

The data mesh is a current technical and organizational concept to enable greater business proximity and more scaling for large organizations involved in data & analytics. Consistent implementation here proves revolutionary and requires change management.

Whether in storage, production or customer service – completely different business processes all involve a use of images which need to be analyzed and evaluated. However, manual evaluation of these images is time-consuming and error-prone. These procedures can be automated with the help of computer vision, i.e. machine analysis and processing of images. Thanks to highly mature methodology, machines are now able to carry out even complicated analyses.

Through creation of analytic applications, SAP Analytics Cloud makes it possible to produce complex reporting scenarios for customers. A building block which decisively complements this function is custom widgets which allow quick and unser-centred creation of visualizations. Here we will show you how to create these and embed them in an SAC application.

If successful proof of concept (PoC) for a data-analysis pipeline is to be followed by production, this often proves to be a long road. Ibis makes it possible to simplify this process and thus add value faster.

After successful local development of a data-analysis pipeline in Python, the code often needs to be rewritten to allow operation in production mode. But does it really have to be that way? Programmed by Wes McKinney, lead author of Python Pandas library, the Python Ibis library provides a fascinating solution for balancing data processing between the production and development environments, thus enabling analytics teams to achieve production faster. This blog post of ours shows how it works.

An MRM solution’s monitoring component offers analysis, evaluation and reporting functions providing transparency about marketing staff's activities with the help of dashboards or configurable reports, for example.

With the help of the workflow component of an MRM solution, marketers create tasks and can assign and control them to themselves or others.This makes timings, responsibilities and the processing status of tasks and campaigns recognizable, filterable and controllable at a glance.

Marketing resource management (MRM) is still too little known among marketers. This is unfortunate, because MRM software can take your marketing planning and management to the next level – provided you find the right software for your needs. To allow orientation in the MRM market, b.telligent as a technology-independent consultancy company has analyzed and classified relevant providers to create a market overview offering assistance, function overviews and provider profiles: b.telligent's MRM market overview.

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.