A Fine Slice Of SQL Server
Last year I benchmarked a few ways of shuffling columns in a data.table, but what about pandas? I didn’t know, so let’s revisit those tests and add a few more operations! pandas winds up being much more competitive than I expected.
Click through for those findings and the code Tom used for the task. H/T R-Bloggers.
Comments closedErik Darling sets MAXDOP to 1:
It’s August, and that means one thing: Family Vacation. I’m taking this month off from regular blogging, and posting some of my paid beginner training content for you to enjoy, while I enjoy not blogging.
Click through for a good video and no text I can use as a helpful graf.
Comments closedErik Svensen gets things organized:
This might be the default way of doing it but if you always want the chart to be sorted by a particular measure even though you are showing another measure then you can use this workaround. You should of course inform the user about the chosen sort order for instance in the subtitle or similar.
Click through to see what Erik has in mind.
Comments closedReza Rad does more than copies:
Copy Activity is one of the most commonly used activities in Microsoft Fabric’s Data Factory Pipeline. The Copy Activity copies the data from a source to a destination. However, there is more to that rather than just a simple copy. In this article, you will learn what Copy Activity is, its rationale, how it works, and its configuration options.
Reza has a video, as well as a demo-heavy full-length article on the topic.
Comments closedSalman Ahmed automates query execution:
In PostgreSQL, there are several ways to execute queries, and one of them is by executing queries from SQL files. This approach allows users to manage and store their SQL queries separately and make debugging and development simpler. Using SQL files also helps in replication of database schemas. This blog discusses how to execute queries from SQL files in PostgreSQL.
Read on to see how you can use the psql command line tool to do just that.
Comments closedA date dimension is extremely useful and is required by most BI applications. This kind of dimension has a key of time level (day, month, etc.), and attributes that describe it such as year, month, etc. In your BI model, you join this dimension to facts on their date fields, to aggregate from day level to week, month, and year.
In this post, I will demonstrate how to create a date dimension on Azure Databricks using Python. A link to the complete Databricks notebook is at the end of the post.
Check out the code, as well as explanation, in that post.
Comments closedSteven Sanderson switches up a visual:
When it comes to data visualization in R, the
par()function is an indispensable tool that often goes overlooked. This function allows you to control various graphical parameters, unleashing a world of customization possibilities for your plots. In this blog post, we’ll demystify thepar()function, break down its syntax, and provide you with hands-on examples to help you create stunning visualizations.
Click through to check it out. My loyalties definitely lie with ggplot2 for static visual development in R but it’s definitely not the only way to get images to look the way you want them.
Comments closedHasan Savran does some looking:
In my previous post, I discussed the process of establishing a Free-text search for Azure Cosmos DB. Towards the end, I demonstrated how to carry out a free-text search using the Azure Portal. Now, I will guide you on how to perform this search using code. To perform this search by code, I created a basic console application and added Azure.Search.Documents and Microsoft.Azure.Cosmos.
Click through for that demonstration.
Comments closedDany Hoter gives us a workaround:
Many users who try ADX in direct query mode encounter errors right away.
The errors complain about lack of memory.
If the tables are small enough, it may work but still performance will not be as advertised on TV.
The reason in most cases is the behavior of joins in ADX as they are created by PBI.
In this article I’ll show different approaches to joining tables as used by PBI for related tables or as can be expressed in KQL in general.
I created a special table in the help cluster with 31 million rows that is big enough to demonstrate the performance differences between the variations.
Read the whole thing. This one’s a little surprising to me.
Comments closedJames Serra has layers, like an onion:
Data lakes typically have three layers: raw, cleaned, and presentation (also called bronze, silver, and gold if using the medallion architecture popularized by Databricks). I talk about this is my prior blog post on Data lake architecture. Many times, companies will create a fourth layer outside of the data lake that I call the relational serving layer. I’ve been having conversations recently with companies about the need for another type of fourth layer, which I will call the physical serving layer. In this blog post I’ll discuss the relational serving layer and the physical serving layer.
Read on to learn more about these.
Comments closed