Friday, 23 February 2018

AAS Snack: process Azure Analysis Services with SSIS

Case
I'm running SSIS and SSAS in Azure. How do I process my tabular model when my ETL has finished? Can I use SSIS to process Azure Analysis Services?
Process Azure Analysis Services



















Solution
One option is to process the Azure Analysis Services (AAS) model is with Azure Automation and a PowerShell Runbook. However the good old Analysis Services Processing Task will also work for AAS and lets you process the model right after the ETL has finished. This post explains how to configure it.

1)  Get AAS Server name
First go to your AAS in the Azure portal and copy the server name from the AAS dashboard. It should look like:
asazure://[region].asazure.windows.net/[Name of Analysis Services Server].
You can also find the models available for processing.
Azure Analysis Services












2) Analysis Services Processing Task
Go to your SSIS project in Visual Studio. Open your SSIS package and add the Analysis Services Processing Task to the Control Flow and give it a descriptive name. Then edit it and optionally add a description.
Add Analysis Services Process Task
















3) New Connection Manager
Go to the Processing Settings page and click on New to add a new Analysis Services Connection Manager. In the textbox for Server or file name you need to enter/paste the server name from step 1. Then enter the email address and password from the user that can process the model. Select the model you want to process and test your connection. Finally press OK (three times) to return to your task editor.
Add new Connection Manager
















4) Object to process
After adding the connection it is time to add one or more objects from your tabular model to process. Press the Add button and select all required objects. For this example the entire model. Then click OK to close the window and change the Process Options, for example to Full Process. Now you are ready to close the Task editor and test it.
Add Object(s) to process
















5) Testing
Now it is time to test the package by executing it. If successful, you can finish the package by renaming the connection manager and adding package parameters to supply the URL, e-mail address and password.
Executing
















Summary
This post showed you how to process an AAS model with a standard SSIS task. More information about deploying to and executing in ADF can be found here.

Wednesday, 14 February 2018

Cognitive functions U-SQL: text sentiment

Case
U-SQL has cognitive capabilities to analyse a text on sentiment. How does that work? Do I need Azure Cognitive-services?
U-SQL Cognitive Capabilities















Solution
Good news is that you only need Azure Data Lake (Analytics and Store) with a U-SQL job. Downside is that U-SQL does not yet have the full functionality of Azure Cognitive Services, but all the basics are available. This blog post describes the text sentiment analysis, but there is a second text analysis capability for Key phrases extraction which will be handled in an future post.

Please: see our blog post about Image Tagging with U-SQL in Data Lake if you have not yet installed the Cognitive Functions for U-SQL that we will be using for this post.

Starting point
The starting point of this blog post is an Azure Data Lake Analytics (ADLA) that is connected to an Azure Data Lake Store (ADLS) with some texts to analyse. For this example we used a transscript of Obama's Victory speech from 2008, but you could for example also use a transcript of Trump's Davos speech from a few weeks ago. The text file will be stored in an ADLS folder called TextSentiment.

Text sentiment
The text sentiment analysis will return two columns for each row with text. The first column is the sentiment classification: Positive, Negative or Neutral. The second column is a score between negative 1 and positive 1. Where a negative number close to -1 means it is very sure that the text is negative and vice versa where a positive number close to 1 means that it is very sure that the text is positive. This also allows you to take an average on the entire text file to get an overall score.

1) Create new job
On the ADLA overview page click on +New Job and then give it a suitable name before we start coding. A descriptive name allows you to find your script/job in the overview of all jobs.
Create new U-SQL job

















2) Referencing assemblies
The cognitive scripts in U-SQL always start with adding references. For text sentiment we need to add a reference to the assembly "TextSentiment".
// Needed for text sentiment
REFERENCE ASSEMBLY TextSentiment;

3) Extract text file
Next step is to extract the text file with the transcript from the ADLS container and store them in a rowset variable called @speech. Each row in the transcript text file contains one paragraph of text. Therefor the we will use Extractors.Text() and only one string column. We replaced the default delimiter with something that doesn't occur in the text (|-pipeline) and if it does the silent option will ignore it and continue without throwing errors.

The extraction script looks a bit like a T-SQL SELECT statement, but because we are getting unstructured data it starts with EXTRACT instead of SELECT and we need to specify the data type for each column we extract (Schema on Read). The FROM does not get the data from a table, but from a file in the ADLS container called "TextSentiment".
// Get the transcript file from the ADLS container
@speech =
    EXTRACT Text string
    FROM @"/TextSentiment/ObamasVictorySpeechTranscript.txt"
    USING Extractors.Text(silent: true, delimiter: '|');


4) Transform data
The method that analyses the text for sentiment takes one readonly input column and three output columns: the original text, classification and confidence. The confidence column can be turned off/on with the Boolean parameter (see code). The name (or datatype) of the output columns cannot be changed.
//Analyse the text and return classification and confidence
@sentiment =
    PROCESS @speech
    PRODUCE Text,
            Sentiment string,
            Conf double
    READONLY Text
    USING new Cognition.Text.SentimentAnalyzer(true); // True adds the confidence column

5) Output data
Now we can output the data to a file in an ADLS container. In the first output we will see a score per line and in a second output we will aggregate the confidence column to get an overal score.
// Output sentiment per line
OUTPUT @sentiment
TO "/TextSentiment/SentimentAnalyzerObama.txt"
USING Outputters.Csv(outputHeader: true);

// Aggregate the Confidence to get an overall score
// Note: it doesn't take into account the length of
// each row. You can find the length with Text.Length
@average =
    SELECT AVG(Conf) AS OverallSentimentScore
    FROM @sentiment;

// Output overall score sentiment 
OUTPUT @average
TO "/TextSentiment/SentimentAnalyzerObamaOverall.txt"
USING Outputters.Csv(outputHeader: true);
Download the complete script here.

The result
Now the script is ready to run. Click on the submit button and wait for the job to finish. This could take a few moments! Then browse to the ADLS folder and preview the file to see the result.
The result with the sentiment and score per paragraph














An other new options to view the result with the Azure Storage Explorer. This new Microsoft tool allows you to browse to your storage accounts and data lake stores to download the result of your U-SQL query.
Azure Storage Explorer




















Summary
In this post you saw how to analyse texts for sentiment. Analyzing media like Twitter or Facebook or emails to/from your helpdesk is probably more interesting then speeches from presidents of the United States. Some might say that the overall score with the AVG is perhaps a bit arbitrary because it shows the confidence, but combine it with the text length and it will give some good insights on the entire text.

As said before the text sentiment in Azure Cognitive Services - Text Analytics API has some additional options like support of multiple languages and language detection, but we will show that in a future post.

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