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As part of their current modernization and digitization initiatives, many companies are deciding to move their data warehouse (DWH) or data platform to the cloud. This article discusses from a technical/organizational perspective which aspects areof particularly important for this and which strategies help to minimize anyrisks. Migration should not be seen as a purely technical exercise. "Soft" factors and business use-cases have a much higher impact.

Machine Learning Operations (MLOps) is a practice for collaboration and communication between data scientists and operations professionals to help manage production Machine Learning (ML) lifecycles. It involves the principles of DevOps in the ML lifecycle to streamline and automate the process from model development to deployment and monitoring. The intention of MLOps is to develop faster deployment and scaling of ML models in a structured and efficient manner.

You’re probably in the same boat as many of our clients facing a challenge: how best to integrate SAP BW (SAP Business Warehouse) as the data source for Microsoft Power BI (Power BI). It’s not always an easy task, since it requires due attention to diverse factors, potential challenges, and possible ways to tune performance.

We have compiled a thorough guide of best practices and limits, underscored by our extensive in-depth experience into integrating Power BI with SAP BW. Now we offer you a brief insight in this blog.

Lack of resources or technical challenges are often hurdles to establish the value and viability of IoT use cases, and present them later to project sponsors. Even for simple IoT use cases, sometimes weeks instead of days may be needed to produce tangible results. In this blog, we’ll present our IoT kick-starter platform that makes it possible to technically assess simple IoT use cases within a few days.

The PoC has been made, a model ready for production has been trained, and the showcase has inspired all stakeholders. But in order for business cases to be realized with the model, it (and the related processing) must be embedded in the existent (cloud) landscape.

Microsoft describes its IoT Operations as a “Range of modular, scalable, and highly available services” that run on Azure Arc-enabled edge Kubernetes clusters. It includes native integration of other MS services like Event Hub or Fabric, whereby the latter’s been generally available since November 2023. Our blog summarizes the new Azure service’s features and deployment.

It's a done deal: The digital product passport (DPP) is coming. It's quite an elaborate affair, but also a huge opportunity for marketing and sales! As part of a planned Ecodesign Directive, the EU will, from 2027, oblige companies in almost all sectors which manufacture, commission or sell goods and products in the EU to introduce the DPP. Experts predict that five trillion DPPs will be issued annually across the EU in the future. This figure gives an idea of the implications of the EU's decision. Which legally mandatory information the DPP must contain is still being clarified. But one thing is already clear: The DPP will massively change the economy – and in doing so, it offers a completely new touchpoint to the customer.

With the Q2 2023 updated titled "New optimized story experience - unified strories and applications", SAP analytics cloud offers users new ways to develop reports and dashboards even more flexibly and easily in an integrated design enviroment. We'll showyou which new features theupdate provides and how it supports you in creating reports,

Microsoft Fabric Is Now Available! Does It Make Sense To Migrate Now?

The new SaaS solution from Microsoft has the potential to change the world of data. But should you migrate now? If so, what is the best approach? A lakehouse or warehouse? And what must you consider with OneLake? Where are the current hurdles? And what about Azure Synapse? We’ll address all these aspects in this article.

First, there’s a key message for all Azure Synapse customers: Synapse will continue to be fully supported and is not being discontinued. Microsoft has no plans to do so! But we’ll tell you where it still makes sense to look ahead.

Ever since ChatGPT was introduced in late 2022, we have all been thrilled about the possibilities of generative AI and large language models (LLMs). What intrigues people is the incredible ease of generating high quality texts and getting responses to questions, code fragments, etc. You simply write a prompt, which is a text input, feed it to the ChatGPT’s API, and voilà, you have a response.

We are still very much in a generative AI hype cycle, where the benefits of a technology are typically overstated. For businesses, it is important to avoid the attendant pitfalls, and to understand when and how to best use ChatGPT or generative AI solutions. In this blog, we look beyond the hype, and show you an approach to evaluate and implement LLM-based Gen AI use cases.

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.