Blog

As an AWS advanced partner, our intention is to regularly document our findings on solutions for data & analytics solutions in this blog and share them with parties interested in AWS. As a cloud provider, AWS offers us and our clients an ecosystem and services allowing very useful solutions for modern data architecture. Our goal here is not to repeat generally accessible AWS documentation. We instead want to condense our technical and professional experience "on paper".

This blog series starts with the topic: "Sizing a Redshift database for a cloud DWH".

Since the release of ChatGPT and the attention drawn to large language models, we have seen a rapid increase in releases of more models and a rapidly evolving market associated with a use of LLMs. A model's suitability for utilization in a business context depends heavily on the respective use case. In this blog post, we shall take a closer look at the currently most important models, and compare them on the basis of enterprise-relevant criteria so that you can maintain a better overview.

Many users of Google's IoT Core are currently looking for a successor to this service which will expire in August 2023. This blog post shows how Stackable's data platform can be used to create a highly scalable open-source alternative to Google's IoT Core.

The topic of planning is often left out of the definition of a data strategy. Planning is perceived as "too special" because it is strongly departmental, existing concepts can only be applied to a limited extent for planning, and the employed BI tools are often unsuitable for planning. As a result, potential remains untapped. This blog shows how you can fix this, even retroactively.

We have demonstrated in part 1 & part 2 how an AWS data lake is built from scratch and how the data is ingested in a Data Lakehouse. In this blog, we describe how to enforce GDPR law, the Right to be Forgotten (RTBF), in a Data Lakehouse. We make both the data lake and the data warehouse built in the previous blogs compliant with having a user exercise their Right to be Forgotten. Let us first understand what RTBF is.

As we have seen in the previous blog post, we should now have our transformed data in the data lake and have it available in the Glue Data Catalogue. In this blog post, we will first discuss what AWS Lake Formation is and see how we can use it to securely share access to the data.

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.