A Fine Slice Of SQL Server
We create a raster image from a picture and calculating the ratio of the pixels on the scale of grayscale. The more the darker colour is represented in the pixels, the bigger the value. And this value is converted into the vector of values. And each vector is represneted as a violin boxplot.
Click through for an example.
Comments closedDavid Alcock wants to know what choice you make when all choices lead to the same conclusion:
As there is an index that has the LastName and FirstName columns the optimiser has opted for an index seek operator using the IX_Person_LastName_FirstName_MiddleName index, and if I look into the Plan XML I can see that it’s using a trivial plan: StatementOptmLevel=”TRIVIAL”.
This basically means there’s one obvious way to return the query results so the optimiser has avoided the cost of going through full optimisation and has elected to use this plan straightaway.
So what happens if I create an identical copy of that particular index, in fact let’s create five indexes that are exactly the same:
There’s a Mass Effect 3 joke in my intro line. But read on for the answer and also check out Barney Lawrence’s comment for a bit more elucidation.
Comments closedThe Hadoop in Real World team looks at a pair of Hive functions:
Both get_json_object and json_tuple functions in Hive are meant to work with JSON data in Hive.
Let’s create a table with some JSON data to work with. We have created a table named hirw_courses and loaded the below JSON text into the table.
Click through for that script as well as seeing how you use each of get_json_object() and json_tuple() and which might be better-suited for your purposes.
Landon Robinson continues the Spark Starter Guide:
Ranking is, fundamentally, ordering based on a condition. So, in essence, it’s like a combination of a where clause and order by clause—the exception being that data is not removed through ranking , it is, well, ranked, instead. While ordering allows you to sort data based on a column, ranking allows you to allocate a number (e.g. row number or rank) to each row (based on a column or condition) so that you can utilize it in logical decision making, like selecting a top result, or applying further transformations.
One very common ranking function is row_number(), which allows you to assign a unique value or “rank” to each row or rows within a grouping based on a specification. That specification, at least in Spark, is controlled by partitioning and ordering a dataset. The result allows you, for example, to achieve “top n” analysis in Spark.
One minor adjustment I’d make is not calling the output of ROW_NUMBER() “Rank” because then it’d make me think that’s the output of the RANK() window function. In the event of ties, those two outputs will differ.
Eitan Blumin wants to pump your passwords up:
This script generates various permutations and variations of common and weak passwords and uses the PWDCOMPARE function to check whether there are any enabled SQL logins that have these passwords.
Read on for several notions of what might constitute a weak password and notes on how to use this.
Comments closedGerhard Brueckl avoids manual processes:
In the past I have been working on a lot of different Power BI projects and it has always been (and still is) a pain when it comes to the deployment of changes across multiple tiers(e.g. Dev/Test/Prod). The main problem here being that a file generated in Power BI desktop (
.pbix) is basically a binary file and the metadata of the actual data model (BIM) cannot be easily extracted or derived. This causes a lot of problems upstream when you want to automate the deployment using CI/CD pipelines. Here are some common approaches to tackle these issues:
Click through to see several bad to palatable options and then check out Gerhard’s solution, which is significantly better. CI/CD is a huge pain point for Power BI developers but people like Gerhard are doing what they can to help.
Comments closedKendra Little has a documentation change of note:
We now explicitly define ‘requests’ and ‘workers’ in the Azure SQL Database documentation, and we’ve cleaned up multiple places where we used to equate the two terms. In this post, I share the history of the two terms when it comes to Azure SQL Database, why the two were ever equated, and why things like this are tricky to change.
There’s a bit of behind-the-scenes around documentation work as well and the types of challenges you might run into when developing software for the public.
Comments closedSaveen Reddy has a few pointers for us:
Configure cost control and set daily, weekly or monthly Workspace Budget limits for serverless SQL pools. This easy-to-use management tool allows you to define and allocate your data assignment to ensure you never exceed your limits.
Click through for a post with animated GIFs for each tip.
Comments closedGilbert Quevauvilliers wants to see who’s using what tables:
I was reading through the blog post Announcing on-demand loading capabilities for large models in Power BI and I got a thought would it not be great to better understand which columns and tables are being used in my Power BI Premium/Premium per user datasets?
To do this, using the new DMV I could now look at the temperature of the tables-column.
The higher the temperature the more the table-column is being used in my reports!
Click through to see how Gilbert put this together but also pay attention to the caveats.
Comments closedPaul Andrew continues a series on applying data mesh principles in Azure:
This principal is very broad, so I want to break down the theory vs practice as before. The idea of self-service is always a goal in any data platform and the normal thing for analytics is to focus on this within the context of our data consumption. Whereby a semantic layer technology can be used in a friendly business orientated, drag-drop type environment to create dashboards or whatever.
However, my interpretation of ‘self-serve’ for a data mesh architecture goes further than just the dashboard creation use case. This should not just apply at the data consumption layer, but all layers within the solution and for clarify, not just related to the data itself. Hence the term in this principal ‘data infrastructure as a platform’. This then unlocks the deeper implication of this serving for a data product, all abstracts of the platform can be consumed in a self-service manner from a series of predefined assets. Let’s think about this serving more like an internal marketplace or catalogue of assets for delivering everything the data product needs to enable a new node within the wider data mesh.
Read on for some deep thoughts on the topic.
Comments closed