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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.

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.