A Fine Slice Of SQL Server
Bala Priya C shares some one-liners:
NumPy’s array operations can help simplify most common time series operations. Instead of thinking step-by-step through data transformations, you can apply vectorized operations that process entire datasets at once.
This article covers 10 NumPy one-liners that can be used for time series analysis tasks you’ll come across often. Let’s get started!
Click through to see the ten in action.
Leave a CommentMarco Russo and Alberto Ferrari answer a question:
In a previous article, Choosing between DISTINCT and VALUES in DAX, we explained how to choose the proper function to iterate the unique values visible in a column in the current filter context. We suggest reading that article before starting this one, because it describes the same underlying problem. Here, we discuss whether to use VALUES in an iterator. This choice depends on the answer to this question: Do you want to include or exclude the blank row generated by an invalid relationship when iterating over the rows of a table reference?
Read on to see how the answer affects your decision.
Leave a CommentHaroon Ashraf gives us a somewhat unwieldy process:
As a SQL/BI developer, I want to run and store my SQL scripts and documentation efficiently in a Notebook as an alternative to using Azure Data Studio SQL Notebooks since Azure Data Studio is retiring soon. Read on to learn more about Visual Studio Code Polyglot Notebooks.
I liked the simplicity of having a SQL kernel in Azure Data Studio. Haroon shows how to work around it and get to roughly the same spot, but I do hope the SQL Server tools team is able to migrate that SQL kernel over to VS Code prior to Azure Data Studio’s ultimate demise.
Leave a CommentSairam Yeturi reduces ETL and ELT requirements:
Organizations often face challenges when trying to scale analytics across large volumes of data stored in centralized SQL databases. As business teams demand faster, more tailored insights, traditional reporting pipelines can become bottlenecks. By adopting Lakehouse architecture with Microsoft Fabric, business groups can mirror their SQL data into OneLake and organize it using the Medallion architecture—Bronze, Silver, and Gold layers. Materialized lake views play a crucial role in this setup, enabling automated, declarative transformations that clean and enrich data in the Silver layer. This empowers teams to build reliable dashboards and AI-driven insights on top of curated data, all while maintaining performance, governance, and security on a scale.
In this post, we will cover how enterprises can use materialized lake views to streamline data orchestration and enhance data quality, monitoring across silver and gold layers, while mirroring their SQL DB tables to Fabric in the Bronze layer.
The best use case for this is a scenario in which your underlying data is already essentially in a star schema or at least easily transformable into one, and you have no interest in modifying the data in the view directly. Do read the limitations before digging in, though, as there are some big ones.
Leave a CommentErik Darling takes an academic concept and explains what it means in practice for SQL Server. Erik does a good job describing the concepts of atomicity, consistency, isolation, and durability. I do agree with Erik’s take on consistency, which tends to be the property that database platforms minimize in return for scalability. The descriptions of all four are good, though Erik has a lot more content that digs into consistency and isolation.
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