A Fine Slice Of SQL Server
Olivier Van Steenlandt has lost a few dimensions in the couch cushions:
A colleague reached out last week while connecting to one of our SQL Server Analysis Services models in Excel. He couldn’t find the expected Attribute folders in the model. He was looking for the following dimensions:
Of particular interest was that this colleague could not see them but Olivier could. The answer ends up being a bit surprising.
Comments closedErik Darling answers the question, why is it so hard to figure out why my query sometimes sucks:
Sometimes people will ask me penetrating questions like “why does SQL Server choose a bad execution plan?” or “why is this query sometimes slow?”
Like many things in databases, it’s an endless spiral of multiverses (and turtles) in which many choose your own adventure games are played and, well, sometimes you get eaten by a Grue.
In this post, I’m going to talk at a high level about potential reasons for both.
Read on for a smorgasbord of factors to consider based on the steps SQL Server takes.
Comments closedSteven Sanderson builds a scatter plot:
Data visualization is a powerful tool for gaining insights from your data. Scatter plots, in particular, are excellent for visualizing relationships between two continuous variables. But what if you want to compare multiple groups within your data? In this blog post, we’ll explore how to create engaging scatter plots by group in R. We’ll walk through the process step by step, providing several examples and explaining the code blocks in simple terms. So, whether you’re a data scientist, analyst, or just curious about R, let’s dive in and discover how to make your data come to life!
Click through for several examples of plot generation.
Comments closedMark Seemann is not a big fan of Entity Framework:
When I evaluate whether or not to use an ORM in situations like these, the core application logic is my main design driver. As I describe in Code That Fits in Your Head, I usually develop (vertical) feature slices one at a time, utilising an outside-in TDD process, during which I also figure out how to save or retrieve data from persistent storage.
Thus, in systems like these, storage implementation is an artefact of the software architecture. If a relational database is involved, the schema must adhere to the needs of the code; not the other way around.
To be clear, then, this article doesn’t discuss typical CRUD-heavy applications that are mostly forms over relational data, with little or no application logic. If you’re working with such a code base, an ORM might be useful. I can’t really tell, since I last worked with such systems at a time when ORMs didn’t exist.
Read on for a thoughtful argument. The only critique I have is I’d prefer stored procedures over saving SQL queries in the code.
1 CommentErin Stellato has an update for us:
We’re rolling towards conference season and the Azure Data Studio engineers have been working hard on two things:
- adding new functionality
- improving stability and performance within the application
Read on to see what they’ve pushed out with Azure Data Studio 1.46.
Comments closedUmair Shahid takes us through the concept of incremental sort in PostgreSQL:
Incremental sort is a database optimization feature, introduced in PostgreSQL 13, that allows sorting to be done incrementally during the query execution process. Sorting is a common operation in database queries, often necessary when retrieving data in a specific order. PostgreSQL’s query planner uses incremental sort to improve query performance, particularly for large datasets. This feature is enabled by default in PostgreSQL 13 and above.
Read on to see how it works and some good practices which help maximize the likelihood that you can take advantage of the feature.
Comments closedDennes Torres asks and answers a question:
I explained in a previous article how the Tables in a lakehouse are V-Order optimized. We noticed this configuration depends on our settings, which can be enabled or not.
One question remains: How could we check if the tables are V-Order optimized or not?
Read on for the answer, as well as a link containing more information on V-Order optimization.
Comments closedWade Waldron talks about a (free) new course:
Recently, I got my hands dirty working with Apache Flink®. The experience was a little overwhelming. I have spent years working with streaming technologies but Flink was new to me and the resources online were rarely what I needed. Thankfully, I had access to some of the best Flink experts in the business to provide me with first-class advice, but not everyone has access to an expert when they need one.
To share what I learned, I created the Building Flink Applications in Java course on Confluent Developer. It provides you with hands-on experience in building a Flink application from the ground up. I also wrote this blog post to walk through an example of how to do dataflow programming with Flink. I hope these two resources will make the experience less overwhelming for others.
Click through for the blog post and check out the full course if you’re so inclined.
Comments closedSteven Sanderson divvies out buckets for a histogram:
Histograms divide data into bins, or intervals, and then count how many data points fall into each bin. The
breaksparameter in R allows you to control how these bins are defined. By specifying breaks thoughtfully, you can highlight specific patterns and nuances in your data.
Click through to see how you can use the breaks parameter in a few different ways to customize your histogram. The default breaks in R are often reasonable, but trying a few different breaks can help you get a better understanding of the actual distribution of the data.
Comments closedSimon Rowe explains what a Sankey diagram is:
In the image, you can see how Sankey used arrows to show the flow of energy with the widths of the shaded areas proportional to the amount of heat loss as it progresses through the engine’s cycle. This series of complex relationships would be difficult for a reader to understand at a glance were they simply presented in text and data tables. Making just such a sophisticated system easier to understand is the purpose of a Sankey diagram, which visually summarises the volume and direction of flows through the stages of a process or system.
Click through for several good examples, some advice on when a Sankey diagram could make sense, and the times in which you should not use this visual.
Comments closed