A Fine Slice Of SQL Server
Christian Lorentzen makes a swap:
With its newest release 1.1.0, the Python package model-diagnostics got the concept of a plotting backend. Before this release, all plots were constructed with matplotlib. This is still the default. But additionally, the user can now select plotly, if it is installed.
Click through to see how it works.
Comments closedVlad Drumea blames the intern:
This is pretty useful if you’ve inherited a SQL Server instance running on Linux, but the last person™ didn’t bother saving the sa password in your teams password manager vault.
Or, if you’re like me, and spin up test instances with random passwords for sa that you don’t bother saving anywhere.
Click through to see where you can find out how to reset the password, and then the actual mechanics of password reset.
Comments closedAaron Bertrand makes use of an ordering:
In my previous post, I showed how to borrow a snake draft concept from fantasy football, or a packing technique from the shipping industry, to distribute different portions of a workload to run in parallel. In the previous example, we determined a distribution order for databases based on size – though you can rank by literally any attribute (or combination of attributes). Once we’ve determined how to build out this order, we may want to store that data somewhere because, sometimes, the source of that data is not directly accessible.
Read on for tips on storing the results in a table, querying the results, and using them to drive SQL Agent jobs.
Comments closedTeo Lachev shares some advice:
Alerting is an important monitoring task for any ETL process. Azure Data Factory can integrate with a generic Azure event framework (Azure Monitor) which makes it somewhat unintuitive for ETL monitoring. You can set up and change the alerts using the ADF Monitoring hub.
Read on for five pieces of advice, in particular, covering how to set up one of these alerts.
Comments closedReza Rad hogs the photocopier:
Microsoft Fabric offers an end-to-end SaaS analytics solution; however, the world is using all kinds of data sources in its implementation. Mirroring is a new functionality in Fabric that allows customers to keep their data wherever they are, but then they can use Fabric analytics solutions with the same speed and performance as if their data were in Fabric. Best of all, this won’t cost extra. If you wonder what it is and how it works, read this article.
Click through for the video and article.
Comments closedDanaraj Ram Kumar breaks out the Python IDE:
There are several approaches to work with Microsoft Purview entities programmatically, especially when needing to perform bulk operations such as documenting a large number of tables and columns dynamically.
This article shows how to use the Python SDK for Purview to programmatically document Purview table columns in bulk – assuming there are many tables and columns that needed to be automatically documented based off a reference tables – as in this example, the data dictionary maintained in Excel.
On the other hand, Purview REST APIs can be used to natively work with the REST APIs whereas the Python SDK for Purview is a wrapper that makes it easier to programmatically interacts with the Purview Atlas REST APIs in the backend.
Click through for sample code and explanations.
Comments closedAndy Brownsword forgets something at home:
Within SSIS you can make use of custom components which aren’t present out of the box. An example of some would be the Azure Feature Pack if you’re working with cloud resources.
These will let us use features not available natively. They can also provide a challenge down the line when we come to deploy changes to the project.
Here we’ll look at an example of this challenge, how to troubleshoot, and ultimately resolve the issue.
Read on for the scenario and fix.
Comments closedSteven Sanderson now has two problems:
Regular expressions, or regex, are incredibly powerful tools for pattern matching and extracting specific information from text data. Today, we’ll explore how to harness the might of regex in R with a practical example.
Let’s dive into a scenario where we have data that needs cleaning and extracting numerical values from strings. Our data, stored in a dataframe named
df, consists of four columns (x1,x2,x3,x4) with strings containing numerical values along with percentage values enclosed in parentheses. Our goal is to extract these numerical values and compute a total for each row.
Click through for a worked-out example.
Comments closedLet’s start with giving some credit, here, since I hate an overdue bill.
ReeceGoding reported and very capably fixed an issue with sp_QuickieStore. The problem only occurred when you ran the procedure with
@get_all_databasesand the new@escape_bracketsparameter set to true. Each loop would add an escape character to the search string, which could end up looking like this:%\\\\\[AnyStringStartingAndEndingWithSquareBrackets]\\\\\]%'
That’s a fix in place, and you can also see updates to sp_HealthParser and sp_PressureDetector.
Comments closedThom Andrews shares a bit of advice:
A problem I’ve seen raised on numerous occasions is users that are getting a non-descript error when they are doing some kind of DML/DDL operation, and they don’t know why they are getting the error, and what is causing it. That error is:
Msg 3609, Level 16, State 2, Line 1
The transaction ended in the trigger. The batch has been aborted.
Read on to learn more about good alternatives, remembering that you’re in the context of some other call when a trigger fires.
Comments closed