1. Machine Learning, Analytics, & Data Science Conference
9/18/2018 3:02 PM
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2. SQL Data Warehouse Create. Load. Integrate
Chris Testa-O’Neill
@ctesta_oneill
Analytics and Data Science Team
This session is brought to you by Microsoft’s Analytics and Data Science Team.
© 2017 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

3. Pre-requisite tutorial setup
Machine Learning, Analytics, & Data Science Conference
9/18/2018 3:02 PM
Pre-requisite tutorial setup
You must have a Windows Data Science Virtual Machine that you can access ( DS2_VS2 size recommended).
You must have a general storage account with a container named datacontainer
This slide is required. Do NOT delete. This should be the first slide after your Title Slide.
This slide should describe what your goals are for this session. This information lets your audience know what you are trying to accomplish with your talk or tutorial—ie, what value will attendees get by investing 25 minutes or 2 hours of their time listening to you.
You should not spend more than 1 minute presenting this slide.
General examples of session goals could be (you will have to create your own specific goals):
Introduce a new technique or approach to solve a customer problem
Compare two approaches and explain why one is superior
Describe a project and the learnings that audience members can apply from it
Teach audience members how to use a specific technology
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4. Machine Learning, Analytics, & Data Science Conference
9/18/2018 3:02 PM
Lab 1: Creating a SQL DW Instance minutes
In this lab, you will perform the following steps
Create an Azure SQL Data Warehouse
Open up the following link and perform all of the steps in the web page the create an Azure SQL Data Warehouse with the firewall settings.
From the lab, name the database AdventureWorksDW and instead of choosing a blank database, select the option of Sample, and select the database AdventureworksDW. Then create a new server with any name of your choice. These instructions are in the text under the graphic. Make a note of the following:
Make a note of your server name: xxxxxx.database.windows.net
Make a note of the admin account
Make a note of the admin password
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5. Lab 2: Scaling and Pausing an Azure SQL Data Warehouse 10 minutes
Machine Learning, Analytics, & Data Science Conference
9/18/2018 3:02 PM
Lab 2: Scaling and Pausing an Azure SQL Data Warehouse 10 minutes
In this lab, you will perform the following steps
Scale and Pause an Azure SQL Data Warehouse
Scale and Pause an Azure SQL Data Warehouse database.
Open up the following link and perform the steps in the web page to scale and pause an Azure SQL Data Warehouse Database AdventureWorksDW.
NOTE: To keep you costs low. Before pausing the SQL Data Warehouse, ensure the DWU settings are no more than 400 DWU before pausing the server.
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6. Machine Learning, Analytics, & Data Science Conference
9/18/2018 3:02 PM
Lab 3: Creating a Database minutes
This Lab will show you how to create a database named EquityDB in Azure SQL Data Warehouse.
Open up your Data Science Virtual Machine.
Copy the following file 1. Create_Database.sql to your desktop from the following location
Open up the file 1. Create_Database.sql in SQL Server Management Studio connecting to the Azure SQL Data Warehouse instance you have already created and execute the script to create the EquityDB – approximate execution time is three minutes
In SQL Server Management Studio, in Object Explorer, right click Databases and click Refresh.
Confirm that the EquityDB database exists in Object Explorer
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7. Machine Learning, Analytics, & Data Science Conference
9/18/2018 3:02 PM
Lab 4: Creating Tables minutes
In this lab you will create three table in Azure SQL Data Warehouse named Dates, Business Hierarchies and EquityTimeSeriesData in the database EquityDB.
Ensure that your Data Science Virtual Machine is open
Copy the following file 1. Create_Tables.sql to your desktop from the following location
Open up the file 1. Create_Tables.sql in SQL Server Management Studio connecting to the Azure SQL Data Warehouse instance you have already created and read through the script. Note the Distribution clause in the sql statement
In SQL Server Management Studio, in Available Databases, ensure EquityDB is selected.
Execute the script to create the Tables – approximate execution time is one minutes
In SQL Server Management Studio, in Object Explorer, click Databases, then expand EquityDB to view the 4 tables listed:
[dbo].[BusinessHierarchies]
[dbo].[Dates]
[dbo].[EquityTimeSeriesData]
[dbo].[Product]
Leave SQL Server Management Studio open
NOTE: The focus in these scripts should be on the DISTRIBUTION clause, but also call out the index clause as well
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8. Machine Learning, Analytics, & Data Science Conference
9/18/2018 3:02 PM
Lab 5: Loading data with PolyBase minutes
LOADING THE PRODUCT DIMENSION TABLE
A. Delete the Product table in the EquityDB database
In SQL Server Management Studio, in Object Explorer, click Databases, then expand EquityDB, and then expand tables to view the Product table.
Right click the Product table and click on the Delete.
In the Delete dialog box, click OK. Confirm that the Product table is deleted.
B. Copy the Product.txt and 4. PolyBase_Load_Date.sql to you desktop
1. Copy the following link in Internet Explorer and download the file Product.txt and 4. PolyBase_Load_Product.sql to your desktop
C. Copy the Product.txt file from your machine to the datacontainer in the General Azure Blob Store
Log into the Azure Portal, browse to your Azure Storage Account and click on the container datacontainer
Click on the Upload button, browse to the Product.txt file on the Desktop and click on OK. Confirm that the Product.txt file appears in the container
Create a master key and a database credential in the EquityDB database
Open up the file 4. PolyBase_Load_Product.sql in SQL Server Management Studio connecting to the Azure SQL Data Warehouse instance you have already created and ensure that the EquityDB database is selected in Available Databases – A dialog box will appear as you open the file, click Yes, for this session only. Read through the script.
Under A: Create a master key. Highlight the code CREATE MASTER KEY, and click Execute– approximate execution time is 30 seconds
Read the code to create a DATABASE SCOPED CREDENTIAL named AzureStorageCredential Note: You must replace the value in the Secret clause with the Access Key of your Azure Storage account. Then click Execute.
D. Create the PolyBase supporting objects
Execute the script to create a CREATE EXTERNAL DATA SOURCE named AzureStorageProduct– approximate execution time is 30 seconds
Execute the script to create a CREATE EXTERNAL FILE FORMAT named TextProduct – approximate execution time is 30 seconds
Execute the script to create a CREATE EXTERNAL TABLE named dbo.ProductExternal – approximate execution time is 45 seconds
E. Query the External Table
Execute the select statements to ensure there are records being queried by the external table
F. Load the data into AdventureWorksDW using PolyBase
Execute the following script:
CREATE TABLE dbo.Product
WITH (
CLUSTERED COLUMNSTORE INDEX,
DISTRIBUTION = ROUND_ROBIN ) AS
SELECT * FROM [dbo].[ProductExternal];
approximate execution time is 45 seconds
Execute the code that creates the statistics objects
G. Confirm that the DimDate table has been recreated
In SQL Server Management Studio, in Object Explorer, click Databases, then right click EquityDB and click Refresh, and then expand tables to view the Product table.
Right click the Product table and click on Select Top 1000 Rows.
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9. Machine Learning, Analytics, & Data Science Conference
9/18/2018 3:02 PM
Lab 6: Using Azure Machine Learning with Azure SQL Data Warehouse 20 minutes
In this lab, you will perform the following steps
Integrate Azure SQL Data Warehouse with Azure Machine Learning
Create an Azure SQL Data Warehouse
Open up the following link and perform all of the steps from one of the web page below to integrate an Azure SQL Data Warehouse with Azure Machine Learning.
Create an S1 Standard Machine Learning Workspace
Click on the following url:
Perform the steps under To create a workspace only
Perform a Machine Learning Experiment
Use the Product table from your Azure SQL Data Warehouse Instance you have created to perform the Machine Learning experiment using the workspace created in the previous step
Go to the following url and sign in
In the top toolbar, to the right of the ? icon, ensure that the workspace you created in the previous step is selected
Perform the steps in the following url:
integrate-azure-machine-learning NOTE: The key objective here is to show how to import data from SQL Data Warehouse, not the model itself
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10. @ctesta_oneill https://ctestaoneill. wordpress. com https://channel9
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© 2017 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.