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In smart manufacturing, a scalable edge platform and the ability to quickly implement business-relevant use cases are crucial for success. With Azure IoT Operations and Microsoft Fabric, Microsoft offers two revolutionary technologies that provide real-time insights into production. Learn how these solutions unlock the potential of use cases such as predictive maintenance, process optimization and generative AI, transforming them from concepts into real value drivers.

With Snowflake Document AI, information can be easily extracted from documents, such as invoices or handwritten documents, within the data platform. Document AI is straightforward and easy to use: either via a graphical user interface, via code in a pipeline or integrated into a Streamlit application. In this article, we explain the feature, describe how the integration into the platform works and present interesting application possibilities.

You want to manage your infrastructure with Terraform, but then it happens – manual changes are made, and you need to find a solution. How to handle this depends on the specific case.

One of Terraform’s greatest strengths is its ability to handle changes made outside its managed resources. The keywords are: data, import, removed, ignore_changes, lock, variables.

SAP data integration is complex: performance issues, storage limitations, and third-party system integration pose significant challenges for businesses. With our flexible framework, you can create a powerful and scalable solution that seamlessly complements SAP BW and S/4HANA—efficient, future-proof, and cost-effective.

Learn how to extend AWS Redshift capabilities with minimal complexity by using Lambda, Fargate, and SQS.

Many security considerations involving Azure revolve primarily around network security. Other important security aspects to be considered in the context of Microsoft Fabric are indicated below.

General Requirements of Enterprises

Many companies with SAP source systems are familiar with this challenge: They want to integrate their data into an Azure data lake in order to process them there with data from other source systems and applications for reporting and advanced analytics. The new SAP notice on use of the SAP ODP framework has also raised questions among b.telligent's customers. This blog post presents three good approaches to data integration (into Microsoft's Azure cloud) which we recommend at b.telligent and which are supported by SAP.

First of all, let us summarize the customers' requirements. In most cases, enterprises want to integrate their SAP data into a data lake in order to process them further in big-data scenarios and for advanced analytics (usually also in combination with data from other source systems). 

How can I integrate data sources that are secured via private endpoints into Fabric? How do I deal with Azure Data Lakes behind a firewall? This blog post shows the possibilities which Fabric Nativ offers

Azure AI Search, Microsoft’s top serverless option for the retrieval part of RAG, has unique sizing, scaling, and pricing logic. While it conceals many complexities of server based solutions, it demands specific knowledge of its configurations.

Polars: Develop Faster, Execute Faster

Polars, the Pandas challenger written in Rust, is much faster, not only in executing the code, but also in development. Pandas has always suffered from an API that "grew historically" in many places. Polars is completely different: it ensures significantly faster development, since its API is designed to be logically consistent from the outset, carefully maintaining stringency with every release (sometimes at the expense of backwards compatibility). Polars can often easily replace Pandas: for example, in Ibis Analytics projects and, of course, for all kinds of daily data preparation tasks. Polars’ superior performance is also helpful in interactive environments like Power BI.

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.