A Fine Slice Of SQL Server
In my last post, we worked out how to change the data type of a field if it already existed and was found to need changing. This time, I want to add a fresh, new column to an existing table.
Read on to see how Snowflake allows you to use IF NOT EXISTS syntax to do this, as well as its limitations in practice.
Kelly Broekstra describes a common flow for business intelligence projects:
I have worked as a business intelligence developer for several years, and I’m always asked: “How do you convert user requirements to a functioning data model?”
I follow the Kimball methodology. For more information, check out the official pages.
But, here are some specific tips on what works for me.
Click through for those tips.
Leave a CommentKevin Wilkie makes some changes:
When working with data, I usually have an idea of what type of data I will push into a field. Sometimes, for whatever reason, it is decided to change the type of data allowed in the field. Today, I want to show how that’s done in Snowflake.
Click through to learn how, and how it’s not quite the same as SQL Server.
Leave a CommentStephanie Bruno makes a connection:
If you’re new to Snowflake and you need to mirror a Snowflake database in Microsoft Fabric, where do you begin? The steps are straightforward enough, but when trying something new, I often get tripped up by the basics. In this case, the configuration screen for mirroring. The documentation tells us to simply enter the server and warehouse, and provides some helpful information on where to find the details, but I prefer step by step instructions with pictures. If you do, too, then this post is for you.
Click through for a walkthrough.
Leave a CommentKevin Wilkie gets down to tuning:
In our last post, we talked about some of my favorite queries I use in Snowflake to see various items of interest – such as finding the worst-performing queries. For today’s post, though, I want to talk about performance tuning.
Yes, you read that right. We’re going to use query history to do some fun performance tuning.
Click through for two queries that can help you find what you may need to tune.
Comments closedKevin Wilkie does a bit of analysis:
Now that we know so much about getting data from Snowflake using the query history, we should be able to get all kinds of data that will be helpful for us. In the next few queries, I want to provide some queries I use that should make your Snowflake usage a bit more useful.
Click through for those queries.
Comments closedKevin Wilkie is interested in just one thing:
By the table function name, you’re probably wondering “Sherpa, why on Earth do I really need to look at query history based on the session? I have Query_History_By_User which gives me a broader look at my data. Who really cares?”
Great question, my friend! You’re right. This new Query_History_By_Session table function isn’t going to give you a lot of data that you can’t get easier – and more helpfully – with Query_History_By_User. Why did Snowflake provide me this “useless” function?
Read on for the value of this function and an example of querying task history.
Comments closedKoen Verbeeck locks things down:
We are implementing a data analytics solution in Microsoft Fabric. A warehouse is used for the gold layer, and we want to give users access to the data. However, by sharing the warehouse, they can read all the data in all the tables. Some data is sensitive, and only users with the correct permissions should be able to view it. Is it possible to implement more granular access control to the data?
Read on for the answer, as well as an important note on how users might be able to circumvent your permissions settings.
Comments closedIn the previous article, I explained SCD (Slowly Changing Dimension) and its different types. In this article, I’ll show you how to implement SCD Type 2 (one of the most common types) using Microsoft Fabric and Power BI. This article includes using Lakehouse, Dataflow, Warehouse, Data Pipeline, SQL Stored Procedures, Power BI Semantic model, and report in Microsoft Fabric.
Click through to learn more about the structure of a type-2 slowly changing dimension, as well as how you can store and load this information to track changes over time.
Comments closedReza Rad talks slowly changing dimensions:
If you want to use Power BI, Microsoft Fabric, or any other data analytics tools, one of the key concepts to understand when working with a data warehouse system is the SCD (Slowly Changing Dimension). I will do this in a series of at least two articles. The first one (this one) will be on the concept of what SCD is, its meaning, and its different types. Then, the next one will discuss how to implement SCD types (such as Type 2) using Microsoft Fabric and Power BI.
Reza focuses on SCD types 0-4 but does briefly touch on types 5-7 (of which, I’d never heard of SCD type 7).
Comments closed