So, whenever you refresh your model if new records have been changed in or added to the source of the lookup query, your table will automatically include the new values in the filter step in Power Query. It is quite simple, you just need to use the name of the query. So, if I want to reference another query, in a new blank query, then the Power Query M scripts would look like below:. Referencing a column is also quite simple.
When you reference a column you need to mention the referencing query name, explained above, along with the column name in brackets. The result is a list of values of that particular column. The Time dimension I explained was in Minutes.
In this post I show you simple way to create Time dimension supporting Seconds. If you need to add time bands time buckets check this out for more details. In real world SSAS Tabular projects, you need to run many different testing scenarios to prove your customer that the data in Tabular model is correct.
If you are running a Tabular Model on top of a proper data warehouse then your life would be a bit easier than when you build your semantic model on top of an operational database. However it would be still a fairly time-consuming process to run many test cases on Tabular Model, then run similar tests on the data warehouse and compare the results. So your test cases always have two sides, one side is your source database that can be a data warehouse and the other side is the Tabular Model.
There are many ways to test the system, you can browse your Tabular Model in Excel, connecting to your Data Warehouse in Excel and create pivot tables then compare the data coming from Tabular Model and the data coming from the Data Warehouse. But, for how many measures and dimensions you can do the above test in Excel?
If your source database is a SQL Server database, then you need to run T-SQL queries on the database side then match the results of both sides to prove the data in Tabular Model is correct. Straight away, this is going to be a long post, so you can make or take a cup of coffee while enjoying your reading. While I will not cover the other side, the source or the data warehouse side, it is worth to automate that part too as you can save heaps of times.
Perhaps it is not perfect, but, it is a good starting point. If you have a better idea it would be great to share it with us in the comments section below this post. I want to generate and run DAX queries and capture the results.
The first step is to get all measures and their relevant dimensions, then I slice all the measures by all relevant dimensions and get the results. In addition, the concepts of SQL are laid out in a simple, concise language and instructions to help you learn the steps properly. If you head over to Amazon you will see great reviews for one of our favorite SQL guides for beginners.
Through multiple step-by-step examples, the authors take readers from knowing absolutely nothing about SQL to being able to quickly retrieve and analyze data from multiple tables. Moreover, you will find many practical examples of common mistakes to avoid that beginners are prone to, making this one of the best books to learn SQL. Lynn Beighley knows how to write about technical topics in an approachable way. Viescas and Michael J. Hernandez wrote one of the clearest and simplest tutorials on writing effective SQL queries.
This hands-on classic guides readers through creating reliable queries for virtually any modern SQL-based database, which you can also use as a mean to build your own SQL dashboard. The authors demystify all aspects of SQL query writing from simple data selection and filtering to joining multiple tables and modifying sets of data. This practical resource is based on the notion that the ability to write Structured Query Language and work proficiently with data is currently one of the most sought after job skills.https://bomfaloni.tk
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Here, author Sylvia Moestl Vasilik showcases a number of SQL Practice Problems that you will often come across in a real-world context, one of the best SQL books on our list — and one of the most practical. Linoff will make an essential addition to your educational booklist — and is one of the best books on SQL on our list, hands down. If you already have the basics down, there are several excellent books out there to take your SQL to the next level.
If you are at this stage of your journey, it is important to look into the literature that fits your specific relational database management system RDMS. This learning SQL book does a great job of covering the basics. However, it also goes far beyond that. The book is great for various end users, analysts, data scientists and app developers. Readers will find general concepts, practical answers and clear explanations of what the various SQL statements can do. The all-encompassing nature of this book makes it a must for a data bookshelf. This book provides a complete collection of modeling techniques, beginning with fundamentals and gradually progressing through increasingly complex real-world case studies.
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- What I Learned When I Almost Died: How a Maniac TV Producer Put Down His BlackBerry and Started to Live His Life.
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It is a must-read for understanding data warehouse design. Viescas, Douglas J. Steele and Ben J.
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For anyone with basic proficiency in writing structured query language, this industry-acclaimed learning SQL book from John L. Steele, and Ben J. Clothier offers an actionable means to take your skill set up a notch and apply your newfound knowledge to a host of real-world scenarios or situations. SQL Antipatterns: Avoiding the Pitfalls of Database Programming is unique in that it focuses on common SQL programming errors, offering a clear-cut insight into avoiding such mishaps or correcting them should they occur.
Broken down into four practical chapters, this resource is perhaps the best way to learn SQL at a more advanced level.