Load 1 TB into Azure SQL Data Warehouse under 15 minutes with Data Factory
This article applies to version 1 of Data Factory. If you are using the current version of the Data Factory service, see Copy data to or from Azure SQL Data Warehouse by using Data Factory.
Azure SQL Data Warehouse is a cloud-based, scale-out database capable of processing massive volumes of data, both relational and non-relational. Built on massively parallel processing (MPP) architecture, SQL Data Warehouse is optimized for enterprise data warehouse workloads. It offers cloud elasticity with the flexibility to scale storage and compute independently.
Getting started with Azure SQL Data Warehouse is now easier than ever using Azure Data Factory. Azure Data Factory is a fully managed cloud-based data integration service, which can be used to populate a SQL Data Warehouse with the data from your existing system, and saving you valuable time while evaluating SQL Data Warehouse and building your analytics solutions. Here are the key benefits of loading data into Azure SQL Data Warehouse using Azure Data Factory:
- Easy to set up: 5-step intuitive wizard with no scripting required.
- Rich data store support: built-in support for a rich set of on-premises and cloud-based data stores.
- Secure and compliant: data is transferred over HTTPS or ExpressRoute, and global service presence ensures your data never leaves the geographical boundary
- Unparalleled performance by using PolyBase – Using Polybase is the most efficient way to move data into Azure SQL Data Warehouse. Using the staging blob feature, you can achieve high load speeds from all types of data stores besides Azure Blob storage, which the Polybase supports by default.
This article shows you how to use Data Factory Copy Wizard to load 1-TB data from Azure Blob Storage into Azure SQL Data Warehouse in under 15 minutes, at over 1.2 GBps throughput.
This article provides step-by-step instructions for moving data into Azure SQL Data Warehouse by using the Copy Wizard.
For general information about capabilities of Data Factory in moving data to/from Azure SQL Data Warehouse, see Move data to and from Azure SQL Data Warehouse using Azure Data Factory article.
You can also build pipelines using Azure portal, Visual Studio, PowerShell, etc. See Tutorial: Copy data from Azure Blob to Azure SQL Database for a quick walkthrough with step-by-step instructions for using the Copy Activity in Azure Data Factory.
Azure Blob Storage: this experiment uses Azure Blob Storage (GRS) for storing TPC-H testing dataset. If you do not have an Azure storage account, learn how to create a storage account.
TPC-H data: we are going to use TPC-H as the testing dataset. To do that, you need to use
dbgenfrom TPC-H toolkit, which helps you generate the dataset. You can either download source code for
dbgenfrom TPC Tools and compile it yourself, or download the compiled binary from GitHub. Run dbgen.exe with the following commands to generate 1 TB flat file for
lineitemtable spread across 10 files:
Dbgen -s 1000 -S **1** -C 10 -T L -v
Dbgen -s 1000 -S **2** -C 10 -T L -v
Dbgen -s 1000 -S **10** -C 10 -T L -v
Now copy the generated files to Azure Blob. Refer to Move data to and from an on-premises file system by using Azure Data Factory for how to do that using ADF Copy.
Azure SQL Data Warehouse: this experiment loads data into Azure SQL Data Warehouse created with 6,000 DWUs
Refer to Create an Azure SQL Data Warehouse for detailed instructions on how to create a SQL Data Warehouse database. To get the best possible load performance into SQL Data Warehouse using Polybase, we choose maximum number of Data Warehouse Units (DWUs) allowed in the Performance setting, which is 6,000 DWUs.
When loading from Azure Blob, the data loading performance is directly proportional to the number of DWUs you configure on the SQL Data Warehouse:
Loading 1 TB into 1,000 DWU SQL Data Warehouse takes 87 minutes (~200 MBps throughput) Loading 1 TB into 2,000 DWU SQL Data Warehouse takes 46 minutes (~380 MBps throughput) Loading 1 TB into 6,000 DWU SQL Data Warehouse takes 14 minutes (~1.2 GBps throughput)
To create a SQL Data Warehouse with 6,000 DWUs, move the Performance slider all the way to the right:
For an existing database that is not configured with 6,000 DWUs, you can scale it up using Azure portal. Navigate to the database in Azure portal, and there is a Scale button in the Overview panel shown in the following image:
Click the Scale button to open the following panel, move the slider to the maximum value, and click Save button.
This experiment loads data into Azure SQL Data Warehouse using
To achieve best possible throughput, copy needs to be performed using a SQL Data Warehouse user belonging to
xlargercresource class. Learn how to do that by following Change a user resource class example.
Create destination table schema in Azure SQL Data Warehouse database, by running the following DDL statement:
CREATE TABLE [dbo].[lineitem] ( [L_ORDERKEY] [bigint] NOT NULL, [L_PARTKEY] [bigint] NOT NULL, [L_SUPPKEY] [bigint] NOT NULL, [L_LINENUMBER] [int] NOT NULL, [L_QUANTITY] [decimal](15, 2) NULL, [L_EXTENDEDPRICE] [decimal](15, 2) NULL, [L_DISCOUNT] [decimal](15, 2) NULL, [L_TAX] [decimal](15, 2) NULL, [L_RETURNFLAG] [char](1) NULL, [L_LINESTATUS] [char](1) NULL, [L_SHIPDATE] [date] NULL, [L_COMMITDATE] [date] NULL, [L_RECEIPTDATE] [date] NULL, [L_SHIPINSTRUCT] [char](25) NULL, [L_SHIPMODE] [char](10) NULL, [L_COMMENT] [varchar](44) NULL ) WITH ( DISTRIBUTION = ROUND_ROBIN, CLUSTERED COLUMNSTORE INDEX )
With the prerequisite steps completed, we are now ready to configure the copy activity using the Copy Wizard.
Launch Copy Wizard
Log in to the Azure portal.
Click Create a resource from the top-left corner, click Intelligence + analytics, and click Data Factory.
In the New data factory pane:
- Enter LoadIntoSQLDWDataFactory for the name. The name of the Azure data factory must be globally unique. If you receive the error: Data factory name “LoadIntoSQLDWDataFactory” is not available, change the name of the data factory (for example, yournameLoadIntoSQLDWDataFactory) and try creating again. See Data Factory - Naming Rules topic for naming rules for Data Factory artifacts.
- Select your Azure subscription.
- For Resource Group, do one of the following steps:
- Select Use existing to select an existing resource group.
- Select Create new to enter a name for a resource group.
- Select a location for the data factory.
- Select Pin to dashboard check box at the bottom of the blade.
- Click Create.
After the creation is complete, you see the Data Factory blade as shown in the following image:
On the Data Factory home page, click the Copy data tile to launch Copy Wizard.
If you see that the web browser is stuck at "Authorizing...", disable/uncheck Block third party cookies and site data setting (or) keep it enabled and create an exception for login.microsoftonline.com and then try launching the wizard again.
Step 1: Configure data loading schedule
The first step is to configure the data loading schedule.
In the Properties page:
Enter CopyFromBlobToAzureSqlDataWarehouse for Task name
Select Run once now option.
Step 2: Configure source
This section shows you the steps to configure the source: Azure Blob containing the 1-TB TPC-H line item files.
Select the Azure Blob Storage as the data store and click Next.
Fill in the connection information for the Azure Blob storage account, and click Next.
Choose the folder containing the TPC-H line item files and click Next.
Upon clicking Next, the file format settings are detected automatically. Check to make sure that column delimiter is ‘|’ instead of the default comma ‘,’. Click Next after you have previewed the data.
Step 3: Configure destination
This section shows you how to configure the destination:
lineitem table in the Azure SQL Data Warehouse database.
Choose Azure SQL Data Warehouse as the destination store and click Next.
Fill in the connection information for Azure SQL Data Warehouse. Make sure you specify the user that is a member of the role
xlargerc(see the prerequisites section for detailed instructions), and click Next.
Choose the destination table and click Next.
In Schema mapping page, leave "Apply column mapping" option unchecked and click Next.
Step 4: Performance settings
Allow polybase is checked by default. Click Next.
Step 5: Deploy and monitor load results
Click Finish button to deploy.
After the deployment is complete, click
Click here to monitor copy pipelineto monitor the copy run progress. Select the copy pipeline you created in the Activity Windows list.
You can view the copy run details in the Activity Window Explorer in the right panel, including the data volume read from source and written into destination, duration, and the average throughput for the run.
As you can see from the following screenshot, copying 1 TB from Azure Blob Storage into SQL Data Warehouse took 14 minutes, effectively achieving 1.22 GBps throughput!
Here are a few best practices for running your Azure SQL Data Warehouse database:
- Use a larger resource class when loading into a CLUSTERED COLUMNSTORE INDEX.
- For more efficient joins, consider using hash distribution by a select column instead of default round robin distribution.
- For faster load speeds, consider using heap for transient data.
- Create statistics after you finish loading Azure SQL Data Warehouse.
See Best practices for Azure SQL Data Warehouse for details.
Send feedback about: