A Fine Slice Of SQL Server
Query Store is my favorite way to gather information about problem queries and plans, and I wanted to share some information on the useful metrics I use most.
The first two are obvious, but there’s a difference between them. The last two are not obvious but offer an unusual utility. I also wanted to explain why I use logical reads and mostly ignore physical reads.
Read on for Jared’s list.
Comments closedIn my previous post I showed an elegant way to extract case sensitive data from an XML, but what if we’d like to use that method to extract all case sensitive data?
Here is how we can do it.
Read on to see how.
Comments closedKellyn Gorman continues a series on learning PostgreSQL for Oracle DBAs:
In the previous post, I covered some high-level areas around installation and architecture, but for this post, we’re going to go a little deeper. For the seasoned Oracle DBA, this should feel like we’re stepping into a familiar landscape with just a few different rules. While both PostgreSQL and Oracle Database are robust, feature-rich systems, their physical architecture and internal mechanics diverge in key areas, especially around storage structures, memory architecture, and background processing.
In this post, we’ll break down these differences so Oracle DBAs can feel more comfortable with the shift when they transition between the two.
Click through to see how the two differ.
Comments closedEd Elliott troubleshoots an issue:
I recently encountered an interesting issue with ADF where the publish feature suddenly attempted to republish every single object, claiming they were new, despite having incrementally published changed objects for some time.
We were using the publish feature where you work on a branch until you are happy, then you raise a PR to main, merge to main, and then switch back to ADF and click publish to push the changes to the
adf_publishbranch.
Click through for the answer. I also love how Ed’s tl;dr is “too bad, read it anyhow.”
Comments closedRayis Imayev does a bit of logging:
Azure Data Factory is no exception when it comes to logging options. All your debug or triggered pipeline executions—their parameters passed during execution, statuses, timings, durations, and more, can be monitored natively in Azure Data Studio. Once you immerse yourself in the realm of previously executed pipelines and start seeing all activities, passed input values, processed output results, and variables being transformed into something else that can only be understood by examining internal expressions and many other details, you begin to feel like an investigator meticulously analyzing everything.
Read on to see what kinds of logging options are available and how you can work with them.
Comments closedAndy Brownsword reviews the list:
When running queries, reviewing execution plans, or diving into Query Store it’s not uncommon to see missing index recommendations, particularly for more complex queries. But are they any good?
The answer is yes, but also no. Read on to learn how it is useful, but only if you take into account the foibles of the missing indexes dynamic management view.
Comments closedArthur Breant runs some tests:
You’ve created a fantastic mockup and your client is delighted. You’re ready to move to production with your application. But one question haunts you: how can you ensure that your application will remain stable and functional through modifications and evolutions?
The answer comes down to one word: testing.
Read on to learn how you can perform unit testing, integration testing, and end-to-end testing of Shiny applications in R. H/T R-Bloggers.
Comments closedIvan Palomares Carrascosa gives three ways to deal with imbalanced data:
Here’s the catch: having imbalanced data usually makes analysis processes more difficult, especially for machine learning models that can easily get biased toward the majority class as a result of dealing with data with a remarkably unequal class distribution, thereby ending up becoming an almost “dummy classifier” that assigns the same class to virtually everything — in the most extreme case.
This article shows several strategies to navigate and handle imbalanced datasets using two of Python’s most stellar libraries for “all things data”: Pandas and Scikit-learn.
Click through for those ways, including sample code.
Comments closedAnil Kumar Moka does a bit of data loading:
In our ongoing exploration of Snowflake data loading strategies, we’ve previously examined how to use pandas with SQLAlchemy to efficiently move data into Snowflake tables. That approach leverages pandas’ intuitive DataFrame handling and works well for many common scenarios where you’re already manipulating data in Python before loading it to Snowflake.
In this article, we’re diving deeper into the Snowflake toolbox by exploring the native Snowflake Connector for Python. While pandas offers simplicity and familiarity, the native connector provides a different set of capabilities focused on precision control and Snowflake-specific optimizations. This article explains you when and how to use this more direct approach for everything from small CSV files to massive datasets that would overwhelm pandas.
Click through for the full article.
Comments closedEd Lima combines expensive with expensive:
Integrating Azure API Management (APIM) with Microsoft Fabric’s API for GraphQL can significantly enhance your API’s capabilities by providing robust scalability and security features such as identity management, rate limiting, and caching. This post will guide you through the process of setting up and configuring these features.
API Management is a really neat service, though it’s rather costly. That’s my biggest complaint about it, though it is a doozy.
Comments closed