Category Archives: HDInsight

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:

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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

HDInsight Series Featured Pic

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. 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.

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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.

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

With the rise of HDInsight and other Hadoop based tools, it is valuable to understand how Power BI can help you take advantage of those big data investments. If you need to set up a cluster to work with, check out my previous posts on Setting Up an HDInsight Cluster and Loading Data Into Your New HDInsight Cluster. These posts show how to do this with no scripting required. If you prefer to script, there are a number of resources with sample scripts on doing the same work.

In this article, I will focus on using Power BI Desktop to get data from the Hadoop file structure in HDInsight using a Hive query. I will also be using the restaurant data I loaded as noted in the previous posts. If you need to create a cluster and load data I encourage you to check the following blog posts:

These posts walk through the process of creating a cluster and loading up data.

Connecting to HDInsight Using the Hive ODBC Driver

Before you can connect using a Hive query you need to download the Hive ODBC Hive from Microsoft.  You can find the driver here: http://www.microsoft.com/en-us/download/details.aspx?id=40886. Once you have the driver installed, the connection can be created.

Open Power BI Desktop and click Get Data on the splash screen. This will open the Get Data dialog. Scroll down until you see the ODBC option. (Do not select a Hadoop or HDInsight option. See my previous post on connecting using HDInsight.)

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Click Connect to start the process.

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Here is where the “fun” begins. You get no help creating a Hive connection string. It took some searching and trial and error to figure out what was needed to make this happen. Here are the properties you need:

  • Driver:  Driver={Microsoft Hive ODBC Driver}
  • Host: Host=yourHDInsightservername.azurehdinsight.net (Your HDInsight server name)
  • Port: Port=443
  • Schema: Schema=default (default Hive database schema)
  • RowsFetchedPerBlock: RowsFetchedPerBlock=10000 (This is the default)
  • HiveServerType: HiveServerType=2 (This is the default)
  • AuthMech: AuthMech=6
    • This is the Authentication Mechanism which is Windows Azure HDInsight Service.
  • DefaultStringColumnLength: DefaultStringColumnLength=200 (Default is 32767, this should always be set lower)

Each property is separated by a semicolon. My completed connection string looked like this (Note: I added spaces to fit better in the post.):

Driver={Microsoft Hive ODBC Driver}; Host=hugheshdinsight.azurehdinsight.net; Port=443;Schema=default; RowsFetchedPerBlock=10000; HiveServerType=2; AuthMech=6; DefaultStringColumnLength=200;

Enter the connection string into the dialog and then you will be prompted for credentials. Use the Database option and set the Username and Password. Then click Connect. In my case, I see three tables in the resultset including the sample table. We have connected to our HDInsight cluster using Hive.

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Retrieving Data from HDInsight Using HiveQL

So, getting a list of tables is not really helpful. As you can see, this is the Power Query portion of the Power BI Desktop. Let’s add a HiveQL statement to return only our sales data.

In Applied Steps, click the gear next to Source. This will reopen the From ODBC dialog. Expand the SQL Statement portion and add a SELECT * FROM yourtable  to get our desired result set. Click Ok and check the results again. You should see the tablename.fieldname format for column headers. At this point, you can proceed with more data shaping and prep data for other analytics. Click Close and Load when you are done and it will load the data into the Power Pivot designer in Power BI desktop.

I hope you enjoyed this series through HDInsight and Power BI. It was a great learning experience for me.

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

With the rise of HDInsight and other Hadoop based tools, it is valuable to understand how Power BI can help you take advantage of those big data investments. If you need to set up a cluster to work with, check out my previous posts on Setting Up an HDInsight Cluster and Loading Data Into Your New HDInsight Cluster. These posts show how to do this with no scripting required. If you prefer to script, there are a number of resources with sample scripts on doing the same work.

In this article, I will focus on using Power Query to get data from the Hadoop file structure in HDInsight. I will be using Excel 2013 with the Power Query Add-In. I will also be using the restaurant data I loaded as noted in the three previous posts. If you need to create a cluster and load data I encourage you to check the following blog posts:

These posts walk through the process of creating a cluster and loading up data.

Connecting to HDInsight

First, open a new Excel workbook and click the Power Query tab. Once there, you can find the Azure HDInsight source in the From Other Sources dropdown. Select that option to open the following dialog:

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You will need your storage account in order to continue. Then you will need the storage account key. Once you have added the key you will see that the Navigator opened in Excel on the right.

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It should show the name of your cluster and the default container name. Double click the container name and it will open the Power Query window. It will show all the files available in the container. Even though we have it organized in folders, the view shows all the files. If you have a large amount of files and you don’t want to scroll to find them, you can click the down arrow on the Folder Path column and use the text filter to find the folder you are looking for.

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Now I have the files I want to use in Power Query. If you click the binary link it will open a copy of the file. However, this is not how we want to work with the data as we have multiple files. (If you did this, remove steps up to the Filtered Rows step in the Applied Steps section.) I now have the files I uploaded showing.

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In order to work with them all together we need to Combine Binaries.

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This now merges all the files into a single dataset. You can now do any data shaping you would like using standard Power Query methods such as updating the column names and the data types. You can even add columns such as a Total Amount column. Here is what my final query looks like including the steps I did. Be sure to give your query a meaningful name. (Note that Power Query realized my Transaction Date column was a date dataype and changed it for me.)

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Now click Close & Load and your data will be loaded into Excel. You can change the query to load a model in Power Pivot as well to do additional work with the data.

The next post will walk through using Power BI Desktop to load data using a Hive query.

Uploading Files to an HDInsight Cluster (No Scripting Required)

As I noted in my first post, I am not a fan of scripting. In that post we set up a cluster without using scripts to do so. Now we are going to look at how to upload files without scripts. While this will work for our demo and learning purposes, I would encourage you to use scripting to handle production level loads or even if you want to upload a lot of files. While I am not a fan, it does not mean the scripting may not be a better overall tool. However, when I am trying to learn the functionality or work with system using other tools (in this case Power BI), I find that methods such as these help me be more productive sooner.

Prepping to Load Data Into Your New HDInsight Cluster

A key difference between standard Hadoop and HDInsight is file management. With HDInsight, you can load files into Azure Storage and they can be consumed by the HDInsight cluster. Keeping with the No Scripting Required mantra, we will be using a graphical interface to load files into Azure storage. There are a number of options out there, you need one of them installed. For our example, we will be using the freeware version of CloudBerry Explorer for Azure Blob Storage. Once you have your tool of choice installed you are ready to get some files.

At this point, you need some files to load. I am using some data I created for another demo. My data is in 7 files of daily receipts for my restaurant for a week in March. Once you have the data, we can load that into the cluster.

Loading Data Into Your New HDInsight Cluster

A noted above, the next steps for use will use CloudBerry Explorer to load our data. In this case, I just copied the folder with my files over to the Azure Storage once I connected the tool to Azure.

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Once that is done, we will look at working with the data in Hadoop and with Hive.

Creating an External Hive Table and Querying It

You can create two types of tables using Hive – internal and external. An internal table loads the data into a Hive database. An external table applies a schema to the data without moving it. I will be creating an external table. I like this concept because it applies schema to the files that have been uploaded and allows other tools to interact with that data using HiveQL. When you drop an external table, the data remains because the table represents structure only.

In order to help everyone through this (in particular me), the next sections walk through the steps I took to create my table and select data from it. (This is not a detailed look at Hive, but rather a focus on the process of making HDInsight data available using HiveQL.)

Understanding the Files

The first step was to document the structure of the data in the files. Here is the data that I had in each of the files in column order:

  • Ticket Number – int
  • Ticket Date – date
  • Hour of the Day – int
  • Seat Number – int
  • App Amount – int
  • Entrée Amount – int
  • Non Alcoholic Amount – int
  • Alcoholic Amount – int

My structure was fairly simplistic. Each file represented a day.

Creating the Table

Now that I had the structure, I needed to work out the table DDL. (Reference: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-Create/Drop/TruncateTable) Here is the syntax that I came up with.

DROP TABLE RestaurantSales;
CREATE EXTERNAL TABLE RestaurantSales (ticketnumber int, ticketdate string, hourofday int, seat int, appamount int, entreeamount int, nonalcoholamount int, alcoholamount int)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘,’
STORED AS TEXTFILE LOCATION ‘wasb:///restaurant-data-files’;

The first statement drops the existing table if it exists. Unlike SQL Server, no error is thrown if the table does not already exist, so there is no need to check for existence.

The second statement creates the table. One quick note on the data types. In my case, my date was not recognized as a date in my first attempt. I changed it to string. When I ran my query it returned NULLs in that column. As noted earlier, this is an external table which means it is applying schema. The fields are terminated by a comma (‘,’). The next part is significant because it is part of HDInsight syntax. The location is prefixed by wasb which tells HDInsight we are using Azure Blob Storage for the files. The three forward slashes mean we are using the default container for files. When I uploaded the data, I added a folder called restaurant-data-files which holds the files for HDInsight. It will now apply the schema to all the files in that folder. If new files get uploaded, they would be part of the query as well.

You can run this in the Hive Editor in the HDInsight Console discussed in the previous blog post. You can track your jobs there and see when it completes.

Querying the Table

The final step is checking to see if it worked. I opened a new Hive Editor window and executed the following statement:

select * from RestaurantSales;

Voila! Once the job completed, I was able to click the session link and review the results.

Hopefully you were equally successful creating your first Hive table and query with your data. The next two posts will talk about using Power BI to interact with HDInsight data.