2 Sql Web Server Business Intelligence Advancement Workshop 2008

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2 Sql Web Server Business Intelligence Advancement Workshop 2008 – Although my professional focus is building enterprise-scale BI solutions, I’ve created my share of informal Power BI reports that were quickly constructed, with the goal of creating something “good enough” rather than achieving perfection. This guide is about designing appropriate and formal solutions but these practices apply to any Power BI project that needs to survive future maintenance.

When you need to load data into a data model, you have a few options and the right choice will depend on a few factors. This equation is usually a balance between generating a table quickly and conveniently or using a disciplined approach to obtain reliable data from a sustainable source of records. The following image shows that data can be transformed at the source (or before data is loaded into the source) or within Power Query using Power BI Desktop.

2 Sql Web Server Business Intelligence Advancement Workshop 2008

Life is full of choices and trade-off decisions. Let’s say you need to create a lookup table that contains sales regions and this information does not exist in the source database. You could easily create a new table in Power Query using the “Enter Data” feature, and just enter the sales regions manually. This would solve the immediate problem with very little effort, but how will the table be maintained if sales regions are added or changed in the future? We could keep the list of values ​​in an Excel file stored in SharePoint for a business owner to maintain when information changes. You could also go all the way back to the beginning of the process and load a dimension table into the source database. IT professionals might take the hard line and say that all data must flow through the data warehouse regardless of effort and cost. Which of these is the correct choice? …it depends on the scope reporting project, and the long-term ownership of the solution. Sometimes quick and convenient is fine but especially tables that need to scale and handle larger data sizes in the future, the following guidelines are critical.

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Following are the general best practices that I religiously apply when creating queries. After briefly describing each recommended practice, I will analyze it and review some of them in detail.

Parameters are used to make the solution portable. Any connection information, such as file path or database server name, should be stored in a parameter so that it can be changed without modifying query code.

Power Query has built-in optimizations to work with different data connectors. Several connectors support query folding, where Power Query translates query steps into the native query language.

Starting with an SQL query rather than selecting a table or view from the list of database objects will ensure that query folding will not work. When possible, start with a table and if you need to use SQL to prepare data before loading it with Power Query, create a view.

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Incremental Refresh enables the Power BI service to split large tables and load data that changes rather than just the entire table when the dataset is refreshed. This was once a Premium-only feature that now works with shared capacity licensing with datasets up to 1GB in size. Even if you don’t plan to use the Incremental Refresh feature, using a pair of date range parameters allows you to filter large tables and keep the PBIX file size small. After publishing the file to the service, you can update the parameters and load more records.

Create two date/time type parameters named RangeStart and RangeEnd, and then add a date range filter according to these instructions.

Resist the urge to leave out columns you’re not sure you need for reporting. In each query, remove all columns that are not needed early in the sequence of steps applied. The easiest way to do this is to use the Select Columns button on the Home ribbon and deselect columns. To change the selection later, click the gear icon next to the Remove Other Columns step.

Rename using title case for all table names and column names that will be visible in the data model

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Although it may seem trivial, it is absolutely necessary to apply friendly naming conventions to all tables and fields. Chris Webb wrote an excellent post about object naming conventions. As a rule, rename all columns that will not be hidden in the data model, using friendly title names (with spaces and mixed case).

No need to rename primary key, foreign key and other utility fields. After tables are added to the data model, hide those fields to remove clutter and confusion for report developers (even if you are the report developer).

Renaming columns and changing the data types is a time-consuming task but be very careful when checking each column returned by the query.

As query design evolves, you will inevitably create inefficient queries with unnecessary steps. There will always be opportunities to improve the design, often by consolidating and rearranging steps.

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Renaming query steps allows you to understand the function and purpose of each step in the Applied Steps list. This creates a series of self-reported actions that will be easier to maintain down the road.

You can further document query steps by changing the step Description in the Properties window or by adding code comments in the Advanced Query Editor.

Many query steps allow records to flow through without blocking subsequent steps. There are also some transformation steps that must read all the records into memory to apply the transformation.

Actions that support a query folder, which can be translated into the native query language of the data provider, should be taken first. If non-foldable steps cannot be avoided, they should be performed as late as possible in the sequence of query steps.

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These transformations must load all records into memory. They are powerful but have volume and performance limitations. Test them with production-scale source data.

Row-level calculations and derived column values ​​can be done using either Power Query or DAX, but M is a much more capable language for data transformation. Effective data preparation using Power Query will simplify and ease the burden of data model design. Regardless of relative efficiency, if you perform all data preparation and transformation in one place, this can simplify future maintenance. There are less common cases where DAX is the best choice to create calculated tables and calculated columns outside the scope of a single row.

DirectQuery has its place in data model design but should be an exception to the usual pattern of importing data into a data model in memory. DirectQuery tables can work with simple aggregation calculations but do not perform well using many other DAX functions.

Use DirectQuery to aggregate values ​​over very large tables that would otherwise not fit in memory, or to support drilling through to detailed non-aggregated records. These are advanced design patterns that should be treated as rare and exceptional.

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Native SQL queries do not support query folding so should be avoided when possible. This may be an acceptable exception to load low volume query results but generally use a database table or view for query data sources.

Load column data only at the necessary level of granularity so values ​​in the data model will compress. Specifically, reduce date/time values ​​to date only. For a time-level solution, store date and time values ​​in separate columns.

Auto-generated calendar tables in Power BI Desktop are fine for self-service projects but dedicated date tables will provide more flexibility.

If available, use a date dimension table in your data warehouse or source database. Second, generate a date table using Power Query.

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Date tables can effectively be created with DAX functions but if all tables are loaded with Power Query this gives you the convenience of managing all tables centrally.

For each file path, web address or server name in source links; use a parameter. It’s not hard to go back and edit source connection information using the Advanced Editor but the easiest way to build parameter connections is to build them as you go.

Start by enabling the “Always allow parameterization in data source and transformation dialogs” feature on the Power Query Editor page in the Options dialog.

As you build each query connection, for most connection types, you will be prompted to select or create a new parameter.

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Here is an example of the parameters in a demo project. Without modifying any code or editing a query, any of these values ​​can be easily changed.

Here are two examples of parameter relations. For the SQL Server connection, the server/instance name is passed as the first argument to the Sql.Database function. The second example concatenates the folder path (stored in the SourceFolderPath parameter) with the file name to create the fill folder and file path.

The best way to guarantee poor query performance with a relational data source is to start with a hand-written native query (such as SQL) and then perform transformations on the results.

If a query is based on a relational table or view, Power Query can generate the native SQL (and a few other supported languages) with an SQL statement instead of selecting a table or view.

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The following image shows the Power Query Diagnostics results that I describe here: Power BI Query Performance &

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