A Fine Slice Of SQL Server
Marco Russo provides an option:
DAX is not like M when it comes to data manipulation, and it is not supposed to do that. However, if you need something in DAX similar to Table.FromList in M, this blog post is for you.
If you have a list of values in a string in DAX and you want to obtain a table with one row for each item in the list, you can do the following:
Definitely an example of “There’s a better way to do this than to use DAX” but sometimes you’re stuck, I suppose.
Comments closedTom Zika performs an experiment:
In SQL Server, using the
KILLcommand to terminate a session results in an entry being logged in the error log.
This raises the question: Does theALTER INDEX REBUILDcommand with theWAIT_AT_LOW_PRIORITYoption also log its actions in the error log?
Tom does violate Betteridge’s Law of Headlines, but you should read the post regardless.
2 CommentsJosephine Bush continues a series on modern data warehousing:
In today’s data-driven world, having the right tools to manage and process large datasets is crucial. That’s where Azure Data Lake Storage (ADLS) and Azure Data Factory (ADF) come in handy, making it easier than ever to store and transform your data. In this post, I’ll show you how to set up ADLS to store your Parquet files and configure ADF to manage your data flows efficiently.
Read on for an overview of both technologies.
Comments closedSteven Sanderson has me wondering who Greg is and why he gets an expression of his own:
If you’ve ever worked with text data in R, you know how important it is to have powerful tools for pattern matching. One such tool is the
gregexpr()function. This function is incredibly useful when you need to find all occurrences of a pattern within a string. Today, we’ll go into howgregexpr()works, explore its syntax, and go through several examples to make things clear.
Read on to learn more about the global regular expression function and how it works.
Comments closedSandeep Pawar has a script for us:
Sharing a function I have been using to get all the Fabric tenant settings and the description of each setting.
Read on for a quick note and the Python function that does the job.
Comments closedEduardo Pivaral does some work:
Healthy code should not include passwords, keys, or secrets in the source code. Sometimes, developers hard-code sensitive information while testing new features but forget to remove it afterward.
How can we validate code without including sensitive information so we can take action before we publish or share code?
Click through for a couple of options. If you do have GitHub Advanced Security (part of GitHub Enterprise Cloud), you can also create a custom pattern for secret scanning that can include passwords, database connection strings, and the like.
Comments closedSemab Tariq takes us through a pair of replication options:
In the world of database replication, choosing between synchronous and asynchronous methods can have a big impact on how reliable, consistent, and fast your data is.
This blog dives into what these methods are, how they work, and when you might want to use one over the other. Whether you’re trying to keep your data super safe or just want it to move quickly, we’ll break down everything you need to know about synchronous and asynchronous replication in PostgreSQL.
Read on for a quick overview of streaming replication and the differences between asynchronous and synchronous options.
Comments closedAndrew Pruski doesn’t want to run a script:
This all seems a bit manual, doesn’t it?
What if we could automate the building of the image and the push to the container registry when we commit to the Github repository?
Thankfully, with Github Actions…we can do just that!
No more George Jetson-style button-pressing finger injuries for Andrew!
Comments closedErik Darling gives us an overview:
We can surmise a few things from this plan:
- If there are good indexes, SQL Server isn’t using them
- That hash spill is some extra kind of bad news
- Spools remain a reliable indicator that something is terribly wrong
Okay, so I’m kidding a bit on the last point. Sorta.
Read on for the scenario and how Erik would handle this case.
Comments closedSteven Sanderson counts the ways:
Counting words in a string is a common task in data manipulation and text analysis. Whether you’re parsing tweets, analyzing survey responses, or processing any textual data, knowing how to count words is crucial. In this post, we’ll explore three ways to achieve this in R: using base R’s
strsplit(), thestringrpackage, and thestringipackage. We’ll provide clear examples and explanations to help you get started.
I, of course, would commission a 128-node Hadoop cluster and write a few dozen pages of Java code to get the answer.
Comments closed