Extending AWS Redshift’s Data Processing With AWS Compute Services
Learn how to extend AWS Redshift capabilities with minimal complexity by using Lambda, Fargate, and SQS.
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More Insights from b.telligent
Fabric Security: More Than Just Private Endpoints?
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.
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SAP Data Integration Into Microsoft’s Azure Cloud
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).
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Fabric Security: Potentials & Limits of the Network Setup
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
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Sizing and Scaling Azure AI Search
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.
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Efficient Distance Joins in Polars
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.
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