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Curated SQL Posts

Comparing Oracle and PostgreSQL Physical Architectures

Published 2025-06-16 by Kevin Feasel

Kellyn 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.

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Azure Data Factory Publishing Everything instead of Incremental Changes

Published 2025-06-16 by Kevin Feasel

Ed 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_publish branch.

Click through for the answer. I also love how Ed’s tl;dr is “too bad, read it anyhow.”

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Azure Data Factory Data Flow Logging

Published 2025-06-16 by Kevin Feasel

Rayis 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.

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Testing Shiny Applications

Published 2025-06-13 by Kevin Feasel

Arthur 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.

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Handling Imbalanced Data in Python

Published 2025-06-13 by Kevin Feasel

Ivan 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.

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Loading Data into Snowflake via Python

Published 2025-06-13 by Kevin Feasel

Anil 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.

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Fronting Fabric APIs with Azure API Management

Published 2025-06-13 by Kevin Feasel

Ed 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.

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Custom Libraries in Microsoft Fabric Data Engineering

Published 2025-06-13 by Kevin Feasel

Gerhard Brueckl isn’t content with the defaults:

When working with Spark or data engineering in general in Microsoft Fabric, you will sooner or later come to the point where you need to reuse some of the code that you have already written in another notebook. Best practice is to put these code pieces into a central place from where it can be referenced and reused. This way you can make sure all notebooks always use the very same code and it is also easy to develop, update and test the common functions.

As Gerhard mentions, having common notebooks with utilities is fine for when you’re getting started with development, but being able to centralize functions in proper libraries can make that code a lot more useful, not just in the context of the single notebook.

I believe that this does allow for arbitrary code execution, so someone with sufficient permissions to create a notebook and import code from arbitrary locations would be able to execute that code. I think there are ways of limiting this risk (such as not allowing your Fabric hosts to connect to any remote servers other than ones you explicitly allow), but it’s something I’d have to puzzle through.

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Optimizing a Snowflake Data Warehouse

Published 2025-06-12 by Kevin Feasel

Harshavardhan Yedla gives us some guidance:

Optimizing a Snowflake data warehouse (DWH) is crucial for ensuring high performance, cost-efficiency, and long-term effectiveness in data processing and analytics. The following outlines the key reasons optimization is essential:

Read on for some tips around optimizing Snowflake warehouses. A lot of this stays at a pretty high level and doesn’t provide detailed guidance, but it’s a good checklist for thinking about your own situation.

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