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Once created, this allows us to query as a normal view –Ģ – Once you’ve created your source file views, you can now create a view for our Dim Product dimension by joining these together – CREATE VIEW vDimProduct asįrom vProduct P INNER JOIN vProductCategory PG on P.ProductCategoryID = PG.ProductCategoryID Note, whilst this is a basic SELECT statement, you will apply any specific lightweight transformations here, such as replacing NULLs with default strings, for example. Below is an example for the vProduct view of the Product.csv file. Repeat this for each of our source files (Product, ProductModel & ProductCategory). In order to create our logical Dim Product view, we first need to create a view on top of our data files, and then join them together –ġ – Create a view on our source files.
#Show adventureworkslt database diagrams code#
Which returns SQL code to query our file – If you haven’t used Serverless SQL before, you can very easily query the file in the data lake using standard SQL – csv files in the root of my data lake, but in a “proper” environment you’d likely store these as parquet files in partitioned folders to improve performance at larger volumes. In our environment, we will have already landed our AdventureWorks data in our data lake in their raw/native format using integration tools such as Synapse Pipelines or Azure Data Factory. This is produced from the following tables – Simply land your data in the data lake, apply the virtual “views” on top that abstract away the underlying complexity, and you’re away. Serverless SQL Pools let us apply this transformation without having to do any kind of physical data processing. Whilst the schema above isn’t overly complicated, you can see from a dimensional approach that this could be heavily simplified to ease reporting into a star schema such as this – Please see schema below (the current version is slightly different, but this is close enough), courtesy of
#Show adventureworkslt database diagrams full#
In this article I’ll use the AdventureWorksLT schema which is a cut down version of the full one, but more than sufficient to explain the concept. Let’s use the AdventureWorks, that good old sample database as an example. In short, Serverless SQL allows us to create logical constructs across your data lake in the form of views or external tables, and then exposing this denormalised schema to your analysts. Integrated connectivity via the T-SQL interface that offers a wide range of business intelligence and ad-hoc querying tools, including the most popular drivers.”.A familiar T-SQL syntax to query data in place without the need to copy or load data into a specialized store.It enables you to access your data through the following functionalities: “Serverless SQL pool is a query service over the data in your data lake. This is where SQL Serverless, part of Synapse Analytics, enters the room. That’s what this post is about, and shows an approach for building logical star schemas (other data modelling approaches do exist…) that can then present simplified structures for your analysts to build reports on or perform ad hoc queries in an agile manner WITHOUT having to go through the physical processing of the raw data. In the previous post I focused on Power BI/Analysis Services as that querying layer, but what if we didn’t have Power BI? What if you just wanted a really agile, simple way of creating a logical data warehouse schema across your data lake that can be consumed by any tool? In a previous post I blogged about “ Building the Datawarehouse-less Datewarehouse“, which is pattern I’ve always liked wherein we can build a logical star schema across a raw data lake and then query it using our reporting tools.