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Author: Kevin Feasel

Finding what Changed in a SQL Server Cumulative Update

Published 2025-02-28 by Kevin Feasel

Brent Ozar does some sleuthing:

Over the last several years, Microsoft has been putting less and less effort into Cumulative Update documentation. We used to get full-blown knowledge base articles about fixes, but these days, we get a collection of footnotes with deceiving hyperlinks that look like they’re going to lead to more information – but they simply lead back to themselves.

So whenever a new Cumulative Update drops at SQLServerUpdates.com, before I install it, I like to:

  1. Log my dev server’s list of current sys.all_objects, all_columns, messages, configurations, etc to tables
  2. Apply the update
  3. Query the new contents of sys.all_objects, all_columns, etc to see what new ones were added

Click through for a script that does just this. It’s not as good as having detailed patch notes, but it beats having nothing.

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Removing Unnecessary Delete Operations

Published 2025-02-28 by Kevin Feasel

Monica Rathbun truncates some data:

SQL Server performance issues often stem from easy fix bottle necks that can be fixed with the right tuning strategies. This short blog will focus on the DELETE statement.

Click through for the full story. One minor correction that I’d offer is that the TRUNCATE TABLE command is logged, which is why you can roll it back in a transaction. The logging process is much less intensive than with DELETE because of the deferred drop logic that Paul talks about in the link.

That aside, Monica’s key point is absolutely correct: DELETE operations tend to be quite slow, especially as the number of records you need to delete increases. This is where techniques like batching delete operations can help reduce the pain level, but if you’re deleting every (or almost every) row from a table, there’s probably a better method. Unless replication is involved in the mix—in that case, there are no good methods for anything because replication hates us.

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Trying out fabric-cicd

Published 2025-02-28 by Kevin Feasel

Kevin Chant tries a Python package:

In this post I want to cover my initial tests of fabric-cicd. In order to provide some tips for those looking to work with this new offering.

Just so that everybody is aware, fabric-cicd is a Python library that allows you to perform CI/CD of various Microsoft Fabric items into Microsoft Fabric workspaces. At this moment in time there is a limited number of supported item types. However, that list is increasing.

Read on for the test. It currently supports a limit amount of functionality, but it looks promising.

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Scraping SQL Server Version Updates

Published 2025-02-28 by Kevin Feasel

Rod Edwards scrapes patch information:

(just for info, i’ve always refer to the SQL Server Version List (https://sqlserverbuilds.blogspot.com) as Blogspot for some reason)

Doesn’t take long, but it would be nice to not have to bother at all, and I don’t mean by moving everything to the cloud and let MS handle it either before anyone says it. 🙂

The details are just websites, so the html is just plain text, AND the awesome people who provide Blogspot also add the details to a publicly shared google doc, in multiple formats, legends.

So what can we do to make our lives even easier. How about downloading/scraping all of the details that you might ever need, and dropping them into tables somewhere? So when its in SQL, you can then choose to trigger off that however you like?

Read on to learn how.

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Orchestrating Data Pipelines in R with maestro

Published 2025-02-27 by Kevin Feasel

Will Hipson moves some data:

If you look at data orchestration tools today you are bombarded with a dizzying array of software platforms that claim unsurpassed processing capability, AI-readiness, elegant UIs, etc. Apache Airflow is just one example of a popular orchestration platform that scales to meet virtually any orchestration need. And while these claims may be true, I argue it is rarely the case that these gargantuan platforms are needed in the first place. For most data engineers, you probably only need to process a moderate amount of data at a moderate time scale. Moreover, if you’re an R user, you don’t want to have to define your data pipelines using drag-and-drop tools or learn another programming language. Not only will this reduce cloud costs but also development time costs.

Click through to see why Will developed maestro and how it works. H/T R-Bloggers.

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Table Compaction in Apache Spark

Published 2025-02-27 by Kevin Feasel

Miles Cole groups things together:

If there anything that data engineers agree about, it’s that table compaction is important. Often one of the first big lessons that folks will learn early on is that not compacting tables can present serious performance issues: you’ve gotten your lakehouse pilot approved and it’s been running for a couple months in production and you find that both reads and writes are increasingly getting slower and slower while your data volumes have not increased drastically. Guess what, you almost surely have a “small file problem”.

What engineers won’t always sing the same tune on is how and when to perform table compaction.

Read on for a dive into the power of compaction (converting a large number of small files into a small number of large files) and plenty of tips along the way.

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Thoughts on Scaling Elasticsearch

Published 2025-02-27 by Kevin Feasel

Vivek Kumar can’t stop at one:

With the evolution of modern applications serving increasing needs for real-time data processing and retrieval, scalability does, too. One such open-source, distributed search and analytics engine is Elasticsearch, which is very efficient at handling data in large sets and high-velocity queries. However, the process for effectively scaling Elasticsearch can be nuanced, since one needs a proper understanding of the architecture behind it and of performance tradeoffs.

Click through for those considerations and the trade-offs you might see.

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Microsoft Fabric February 2025 Feature Round-Up

Published 2025-02-27 by Kevin Feasel

Patrick LeBlanc tells us what’s new:

There are a lot of exciting features for you this month! Here are some highlights: In Power BI, Explore from Copilot visual answers which lets you do easy ad-hoc exploration. In Data Warehouse, Browse files with OPENROWSET (Preview) and Copilot for Data Warehouse Chat (Preview). For Data Science, AI Skill is now conversational.

These are just some of the great features this month, keep reading to learn about all of what’s happened in Fabric this month.

Click through for the full report.

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Step Outputs to Help Troubleshoot Failed SQL Agent Jobs

Published 2025-02-27 by Kevin Feasel

Jim Evans gives us a reminder:

When troubleshooting SQL Agent jobs, often the Job history output is truncated or poorly formatted, making it hard to read. This is especially true when calling SSIS Packages, running jobs like DBCC CheckDB or when running T-SQL code that returns a lot of output. Are there options to get more readable Job output to aid in troubleshooting?

There are a few settings here that we can use to make troubleshooting SQL Agent jobs a little bit easier. In addition to these, it’s also a good idea to retain more history for longer, especially if you’re not in a position to track those job outputs each day.

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