Minnesota BI User Group – Powering Up HDInsight with Power BI (December 2015)

On Wednesday, December 16, I presented on this topic at the Minnesota BI User Group.  This session is based on five blog posts that I created in August 2015.

You can find the presentation here: Powering Up HDInsight with Power BI (pdf).

The details can be found in the blog posts noted below:

Setting Up and HDInsight Cluster (No Scripts Required)

Exploring the Microsoft Azure HDInsight Query Console (No Scripting Required)

Uploading Files to an HDInsight Cluster (No Scripting Required)

Using Power BI with HDInsight Part 1: Power Query and Files

Using Power BI with HDInsight Part 2: Power BI Desktop and Hive

My goals for this series

1. Document using Power BI with HDInsight

2. Prove that you can set up a HDInsight Cluster with no scripts

Other References from the Session

Azure: http://azure.microsoft.com/en-us/

Cloud Berry: http://www.cloudberrylab.com/free-microsoft-azure-explorer.aspx

 

Thanks for attending my session.

Powering Up HDInsight with Power BI–

On Tuesday, September 15, I presented on this topic for Pragmatic Works. You can find that session here. This session is based on five blog posts that I created in August 2015.

Powering Up HDInsight with Power BI (pdf)https://dataonwheels.files.wordpress.com/2016/02/powering-up-hdinsight-with-power-bi.pdffoundin the

Setting Up and HDInsight Cluster (No Scripts Required)

Exploring the Microsoft Azure HDInsight Query Console (No Scripting Required)

Uploading Files to an HDInsight Cluster (No Scripting Required)

Using Power BI with HDInsight Part 1: Power Query and Files

Using Power BI with HDInsight Part 2: Power BI Desktop and Hive

My goals for this series

1. Document using Power BI with HDInsight

2. Prove that you can set up a HDInsight Cluster with no scripts

Other References from the Session

Azure: http://azure.microsoft.com/en-us/

Cloud Berry: http://www.cloudberrylab.com/free-microsoft-azure-explorer.aspx

Wrap Up from the Session

A few questions were asked during the session and I wanted to handle some of them here.

Why did you not use Azure Resource Manager to deploy storage?

I did this as simple as possible and did not need to use the Resource Manager for my demos. However, if you need to rebuild the cluster quickly, the Azure Resource Manager would be a good option. Find out more here: https://azure.microsoft.com/en-us/documentation/articles/hdinsight-provision-clusters/. This site will also walk through scripts and other options for setting up HDInsight clusters.

Why didn’t the table structure show up in the Power Query demo?

The Power Query demo worked with the data from a file approach. This approach is more “raw”. The files did not have column headers, so no headers were created in the table. However, when working with the Power BI Desktop demo, I used Hive. The table was defined in Hive and were easily seen. This is another case for using Hive or something similar to define the schema for ease of use.

What are the differences between Hadoop, Hortonworks, and HDInsight?

Starting from the top, Hadoop is the Adobe open source specification. All of the products listed above are based on Hadoop.

Hortonworks and Cloudera are examples of Hadoop distributions. These companies have worked with the various versions of open source technologies around Hadoop and created a supported distribution as a result.

Finally, HDInsight is Microsoft’s cloud-based Hadoop implementation. They continue to add functionality including Spark, R, Giraph, and Solr. You can expect Microsoft to continue to grow the capabilities of HDInsight as part of their cloud-based analytics solutions.

Thanks for attending my session.

Power Testing ETL with Power BI – Shaping The Data with Power Query and Power Pivot

This blog post digs into the details of shaping the data with Power Query and Power Pivot in order to build out the test cases. In the previous post, you were able to get a sense of the bigger picture and how the pieces work together. This post will focus entirely on creating the source table that will be used.

One of the most difficult parts of testing the data in an ETL process is that the data needs to be transformed to match the results of the ETL process. Typically this is done using a combination of tools including SQL, Excel, and even Access. The solution I am proposing will use Power Query to do the initial massaging of the data and Power Pivot to put any finishing touches in place.

Understanding the Requirements

The first thing that has to be understood are the requirements. Those requirements are driven from the business rules and the Source to Target Map. Because we are focusing on a non-developer to deliver this work, we need to move away from developer centric tools and into the world of Excel and Power BI.

Building Out the Power Query Query

Power Query is an excellent choice for this work. It allows us to transform or shape the data through a series of steps. What really makes this compelling is that Power Query is a “no code” solution. Once the tester or analyst is familiar with the tool, they understand that most operations can be accomplished using short cut or right-click menus during the design process. Here is the indepth look at what it will take to take the multiple authors in the source and separate them into multiple columns using Power Query.

Step 1 – Find the data source

In our case the data source is a CSV file. You can download that file here. This link will opens an Excel file with the pipe-delimited values that will be used as the source. I would recommend saving it as a .csv file as it is easier to work with in Power Query.

Here is the data raw so you can see what we will be working with:

BookName|Publisher|Genre|Copyright|PageCount|AuthorNames
Wish List|John Locke Books|Thriller|0011|215|John Locke
Riders of the Pale Horse|Bethany House Publishers|Christian Fiction|1994|348|T. Davis Bunn
HTML Pocket Reference|O’Reilly|Technology|2000|92000|Jennifer Niederst
Renegade|Thomas Nelson|Juvenile Fiction|2007|245|Ted DekKer
Gutenberg to Google|Billion Soul Publishing|Missions|2009|272|James Davis
Sinner|Thomas Nelson|Suspense|2008|386|Ted DekKer
SQL Server Analysis Services 2012 Cube Development Cookbook|Packt Publishing|Technology|2013|324|Baya Dewald, Steve Hughes, Paul Turley
Troubleshooting SQL Server – A Guide for the Accidental DBA|Redgate Books|Technology|2011|358|Jonathan Kehaysias, Ted Krueger

Step 2 – Open Power Query in Excel and Connect to the CSV File

Select the Power Query tab and select the From File option on the ribbon. Pick the From CSV option. Select the booklist.csv file and click OK. The result will be a preview of the data, which in our case is all the data. You can see it has created the Source, First Row as Header and Changed Type steps. If it did not do this for you automatically, you may need to set the delimiter and specify that the header is the first row.

Step 3 – Shape the Data in Power Query to Match Our ETL Process

In Power Query, we are going to split the author list and the author names. We also will apply some trimming to the data. In all we will apply ten (10) steps to query. Power Query works like an ETL tool as it shapes or transforms the data a step at a time.

Splitting the AuthorNames column

In this step, we will create a column for each author name. Our destination supports up to five authors. Our source has up to three. Right click on the AuthorNames column, select Split Column, then By Delimiter.

You can leave the defaults in the dialog and click OK.

This will result in three columns being created as AuthorNames.1, AuthorNames.2 and AuthorNames.3. Power Query does the next step which changes the data type to match what it sees in the resulting data.

Splitting the Author’s Names into First and Last Name Column

You will need to repeat this three times, once for each AuthorNames column. What is different is that we need to match a couple of business rules:

1. Author names will be stored as AuthorFName and AuthorLName for up to 5 authors (e.g. AuthorFName1).

2. Authors with middle initials or middle names or variations thereof should store these values with the first name. For example, J.R.R. Tolkien would store “J.R.R.” in the AuthorFName column and his last name, “Tolkien”, will be stored in the AuthorLName column.

Understanding these rules clarify how we should split these columns. Like before we will select to split the AuthorNames.1 column. However, in the delimiter dialog we will use a space as a delimiter and we will also choose the right most delimiter. This will pick the first space from the right, essentially the last name and everything else will be separated.

We will repeat the process for each column. The last step for this process is to rename columns to something meaningful for us to reference later such as the target field names like AuthorFName1. This will make the steps later simpler to follow.

Trim Author First Names for Authors after First Author

The final step we need to do is to apply a trim to the AuthorFName2 and AuthorFName3 columns. When the data is split, leading spaces were retained. In my demos, this is “discovered” as a mismatch in the test scenario. This would be an example of an easy miss for someone not used to some of the nuances of ETL. Keep in mind that we will test the tests as well throughout this process. This is a simple fix in Power Query – Right Click the affected columns and select Transform then Trim. Problem solved.

At this point, we have completed our work in Power Query. Up to this point, you may have seen the results of your query in an Excel spreadsheet. You actually want to load the data to a Power Pivot model. If you right-click on the query in the Workbook Queries panel, you can change the Load To target.

Select Load to Data Model and then we will finish the source data using Power Pivot.

Step 4 – Fix Additional Issues Using Calculated Columns in Power Pivot

Open the Power Pivot model in Excel. You should see data from your Power Query query as one tab of the data. While we have massaged some of the data there are still a few data issues that need to be resolved to match business rules.

3 – Copyright years must be stored as 4 digit values.

4 – Page counts should not exceed 1000.

If you look at the source data you will notice that one of the books has a two digit year for the Copyright. This should not be imported as it does not meet the rule. In our case, we will set the value to NULL in the ETL process. The same is true for one of the book page counts, it is 92,000 which greatly exceeds the maximum page count allowed by the business rule. It too will be set to NULL. The idea here is that row value checks are easily handled in Power Pivot with DAX and calculated columns.

To resolve the copyright year issue we are using the following DAX to create a new column called “Copyright Year”:

=IF([Copyright] < 1900, BLANK(), [Copyright])

To resolve the page count issue, we use the following DAX and create a “Pages” column:

=IF([PageCount]>1000, BLANK(),[PageCount])

Now we have fixed the remaining issues that violate business rules in the Power Pivot model.

Step 5 – Add Some Calculated Measures and Columns that Can Be Used for Data Validation

The final step is to add some calculations that will help us do some basic load testing. The first is just the row count. In this case, I created two measures: Source Book Count and Source Distinct Book Count (This handles a business rule that says a title can only be imported once). We can use these measures to verify that the expected data made it from source to destination. Both of these measures were created in the calculation area in Power Pivot using the Autosum functions from the ribbon. The resulting DAX is noted below.

Source Book Count:

Source Book Count:=COUNTA([BookName])

Source Distinct Book Count:

Source Distinct Book Count:=DISTINCTCOUNT([BookName])

The last calculation we need to create is the Author Count calculated column. This needs to be a column as each row could have a different number of authors. Based on what we know with the data, we will count instances of AuthorLName columns that are not NULL to determine the number of authors.

=IF(ISBLANK([AuthorLName1]),0,1)+ IF(ISBLANK([AuthorLName2]),0,1)+ IF(ISBLANK([AuthorLName3]),0,1)

This calculation would need to be modified if the source had an row with more than three columns.

Shaping Is Complete

The source transformation is now complete in the test scenario. A key point is that no code per se was written. While some DAX was required, it was fairly straightforward and likely the most complicated part of setting up the source table for testing.

Next up, creating the tests with Power Pivot and DAX.

Power Testing ETL with Power BI – The Process

This is a short blog series on using Power BI tools to support testing ETL processes. I have presented on this subject at few SQL Saturdays over the past few years and am finally succumbing to multiple request to turn it into a blog post. Realizing the amount of content is more than I typically would put into a single post, I will be putting together this short series to cover the material. The first post is this one. It will walk through the entire process at a high level. I will follow this post with a deeper look at Power Query’s role in the process. The third post will cover Power Pivot and building out test cases. Finally, we will wrap the series up with some visualization ideas for Excel and Power View. You can find all the posts as they come online here. Let’s get started.

The Problem Area

Why use Power BI to test ETL? While working as the architect on an ETL project for moving data from third party web service to an on-premise financial solution, we needed to put together a testing strategy that could be implemented by non-developers on the project. Our situation was that our project was “too small” to engage our QA team but the requirement for reusable testing needed to be fulfilled. Our project team consisted of a BI architect (that would be me), an ETL developer, and a business analyst (Chuck Whittemore).

NOTE: We are testing the data transformations and data load. This is not intended for auditing or performance. There are other tools for reviewing those including the built in reporting in SSIS and Pragmatic Works’ BI xPress tool. If you are tracking whether a package fails or succeeds, you should use either of these options not this process.

The Big Idea

The BA and I were discussing options for testing and we theorized that we could use a new add-in for Excel (Power Query, still in preview at the time) with Power Pivot to build out tests. The key to success on this project is that we needed to be able to test with non-developer tools, no SQL Server Management Studio or SSIS could be involved in the testing. The primary reason for this is that he would be doing the testing. We also did not want to recreate every step in the ETL process the same way. So, time to put theory into practice. We determined that we would create test cases in Visual Studio then build out tests to match those cases in Excel using the Power BI add-ins. He would do the work in Excel and we, the developer and I, would provide technical support as needed.

The Recommended Tools

Before we dig into the process, I want to lay out the tools used for development and for testing. While this solution can use other tools, it is important to know what we used in practice to create our solution.

ETL Development Tools

The ETL development was done using SQL Server Integration Services (SSIS). At the time, we needed to use Script tasks to consume the web service content. The financial system used a custom load process that we dumped formatted data into a file for the system to pick up and load.

In the examples, I use in the presentations and will lay out here, I will be using a text file to SQL Server implementation. While complex ETL problems are common and hard to test, this simplified version is easier to follow in examples. You should be able to apply the principles used here to test any solution.

Testing Tools

The testing development for the referenced project consisted of Excel with Power Query and Power Pivot. Power Query was in preview at the time, so we had some of the performance issues and early bugs to work through. None of these issues, prevented us from completing the project.

The presentation solution relies on the latest version of Power Query (which changes every month) and Power Pivot in Excel 2013. Most of the examples are easy to follow, but you should be able to solve most transformation tests with the combination of Power Query and Power Pivot. Definitely do not discount the capabilities of Power Query and the fact that new functionality is being added each month.

Team Foundation Server/Visual Studio Online

Both projects use the online version of TFS. If you are currently not using a source control and work tracking solution, I highly recommend you look at the online version of TFS. It will allow you up to 5 users free and give you ability to use source control, create test plans, create test cases, log bugs and track changes. These are key features necessary to complete a good solution that can be managed and tracked.

The Process

I am going to walk through my demo to build out the process steps. This will allow you to see examples. I will call out any thing of relevance related to the project here as well.

1. Business Rules

The first part of any project, especially in ETL, is to understand the business rules. If you are working with a data warehouse project, this may be fairly well documented in a dimensional model. In both of our cases here, we are moving data from one system to another. The transformations and business rules are primarily driven by the target system. Here are some examples of business rules in the media library sample project.

• Author names are stored in separate columns – FirstName and LastName
• If an author’s name include a middle name or initial or some variation, this combination should be stored in the first name column. For example, J.R.R. Tolkien would be stored as follows:
  — FirstName: J.R.R.
  — LastName: Tolkien
• Copyright year should be stored as a 4 digit value
• Page numbers should not exceed 1000

Every project has some type of business rules. It is hard to build out transformations and create test cases without these rules.

2. Source to Target Map

This is the single most important document for the tester. It tells the tester how the developer is getting from source to destination and what type of data massaging needs to be handled. Typically, people use some variation of the example created by the Kimball Group over the years.

3. Developing SSIS

The developer begins the process of creating the SSIS package. He will be using the Source to Target Map as his guide and will update that document to handle special cases in the data as needed. Ideally he is working in a development environment that will allow for test build outs as well.

4. Creating Test Plans and Test Cases

The tester creates test plans and test cases in TFS. These tests are based on business rules and the source to target map. Depending on both the complexity of the solution and the time to develop, some test cases could be did the table move the correct data field for field and row count. This method can be particularly useful when working with large tables or simple data flows. However, you should have a test case for every transformation that massages the data. This will insure that the data is being transformed as expected.

Keep in mind, this solution will support test cases for each field in a data load if required. The tester and architect should evaluate what is the appropriate amount of coverage to guarantee the highest level of quality in the data transform. As always, there is a diminishing rate of return if you “test everything” at the lowest level. It will be expensive in terms of cost of development when the chance for error is minimal. It will also take substantially longer to test everything. You need to understand and be able to articulate how the testing was accomplished and your level of confidence in the results.

5. Building the Tests

This is the most extensive part of the process besides the SSIS development. I will not go into all the details here, but will walk through the overall process and principles. I will provide detailed examples in the follow up posts as noted above.

Let’s start with the end result. Chuck and I were able to determine that we could use DAX to create comparative formulas on data that could be brought into Power Pivot from both the source and the destination. Essentially, we wanted to use math to determine the results of the tests. So in our example, we use a formula like “if Source.CopyrightYear = Destination.CopyrightYear, then it passes, else it fails.” Depending on how you want to measure, pass could be 0 or 1. Then we add the values up to determine if data passed or failed the test. We can even tell you failure rates.

In order to get the data in a comparative state, we needed each table in the destination with a table that matched from the source. However, it is very common that sources and destinations are not one-for-one table matches. This is where Power Query comes in. Using Power Query in our example set we bring in the text file and massage or shape the data to look like the destination. Most importantly, we need to apply all business rules and transformations to the source. Once this is done, we do no massaging on the destination data. This allows us to compare what the ETL process did with what our tests say it should have done.

A key part of being able to compare is the ability to relate the two tables in Power Pivot. You need to be able to match natural keys or derived keys between the two sources. The relationship should be from the destination table to the source table. Without this relationship, you will not be able to build the calculations for the tests. Keep in mind the goal is to get our source to look like expected results. Any data in the destination should match the source in our scenario.

Once both tables are created and loaded into Power Pivot, we can complete the tests using DAX. In some cases, we create calculations on both tables to be compared. A classic example is row count. We count the number of rows in the source table and the destination table. Then we create a calculation on the destination to compare values. This meets the requirements of a row count test case (e.g. all data was successfully imported).

Another example of a test is to compare the content in a field from source to destination. This is where we use a lot of conditional logic to verify the contents of a field in a row is the same in both tables. Calculated columns (not measures) are used to create the comparison results. The conditional statement should result in a number. This is important in order to create a measure that sums up the results to determine if errors exist or not. If you choose success to be 1, then you will check your results against the row count to determine if there are errors. If you choose failure to be 1, then a nonzero count means you have errors. There is no right or wrong way to handle this, you would choose based on visualization techniques. Most of the time, using 1 for failures is fine. However, if you want to create KPIs, you will likely need success to be one so you have a good target to work with.

6. Testing the Initial Load

Once you have created the tests, you are ready to test the initial load. You will connect to both sources. Ideally, your source will not change so you can redo the test multiple times, but this will work regardless. Refresh the data which may require rerunning the Power Query query. Once you have refreshed the data you should be able to check the calculations in a simple pivot table to determine what tests have succeeded or failed. This is the beauty of this solution. Each subsequent execution of SSIS, you will be able to refresh your data and review your results to determine how successful the ETL is.

          

A side effect of this work is that the developer can review the test results in Excel and Power Pivot with you to more easily find the discrepancy in the data transform. In some cases, the tests are in error as well. It is important that the developer and tester work together to determine cause as well. A good team will be able to work through issues rather quickly.

7. Recording Bugs and Issues

You will need to go back to Visual Studio to change the pass/fail for each test. If a test fails you can log a bug for the developer and you that information to determine if it was fixed prior to a subsequent run. It is likely that multiple sprints will be required to complete the work so you can work with your team to determine the best ways to communicate what is ready. If you track the work in TFS, you will queries are available to help you see what work has been completed.

You can determine if the fix worked and then set the test results accordingly. This will help show progress on the project as well.

8. Visualizing the Results

You can visualize your results using KPIs, conditional formatting and even Power View. If you have a project that needs to be easily evaluated you can publish your results to SharePoint and use charts and graphs to show how accurate the process is so far.

 

We will dig into visualization options more in a following blog post.

Tracking Test History

No solution is perfect and that is true here as well. One of the most common questions is how do we see the historical results? This solution does not easily provide for that. I am looking at options, but for the moment the idea is that the history will be tracked through TFS. However, you could save the workbook after each iteration. This will give you some history, but you would want to make sure that you don’t refresh data on a historical workbook or the results would be overwritten.

Some final thoughts.

Power Query is not an ETL tool. It’s target destination is always the same – Power Pivot. While it’s ease of use makes it appear to be a tool to be used for ETL, it is not there yet. However, it is in its ease of use that we have a place to work with it here.

My plan is to have some deeper technical dives into parts of the solution in the future.