Load Contoso retail data into dedicated SQL pools in Azure Synapse Analytics
In this tutorial, you learn to use PolyBase and T-SQL commands to load two tables from the Contoso retail data into dedicated SQL pools.
In this tutorial you will:
- Configure PolyBase to load from Azure blob storage
- Load public data into your database
- Perform optimizations after the load is finished.
Before you begin
To run this tutorial, you need an Azure account that already has a dedicated SQL pool. If you don't have a data warehouse provisioned, see Create a data warehouse and set server-level firewall rule.
Configure the data source
PolyBase uses T-SQL external objects to define the location and attributes of the external data. The external object definitions are stored in dedicated SQL pools. The data is stored externally.
Create a credential
Skip this step if you're loading the Contoso public data. You don't need secure access to the public data since it's already accessible to anyone.
Don't skip this step if you're using this tutorial as a template for loading your own data. To access data through a credential, use the following script to create a database-scoped credential. Then use it when defining the location of the data source.
-- A: Create a master key.
-- Only necessary if one does not already exist.
-- Required to encrypt the credential secret in the next step.
CREATE MASTER KEY;
-- B: Create a database scoped credential
-- IDENTITY: Provide any string, it is not used for authentication to Azure storage.
-- SECRET: Provide your Azure storage account key.
CREATE DATABASE SCOPED CREDENTIAL AzureStorageCredential
WITH
IDENTITY = 'user',
SECRET = '<azure_storage_account_key>'
;
-- C: Create an external data source
-- TYPE: HADOOP - PolyBase uses Hadoop APIs to access data in Azure blob storage.
-- LOCATION: Provide Azure storage account name and blob container name.
-- CREDENTIAL: Provide the credential created in the previous step.
CREATE EXTERNAL DATA SOURCE AzureStorage
WITH (
TYPE = HADOOP,
LOCATION = 'wasbs://<blob_container_name>@<azure_storage_account_name>.blob.core.windows.net',
CREDENTIAL = AzureStorageCredential
);
Create the external data source
Use this CREATE EXTERNAL DATA SOURCE command to store the location of the data, and the data type.
CREATE EXTERNAL DATA SOURCE AzureStorage_west_public
WITH
(
TYPE = Hadoop
, LOCATION = 'wasbs://contosoretaildw-tables@contosoretaildw.blob.core.windows.net/'
);
Important
If you choose to make your azure blob storage containers public, remember that as the data owner you will be charged for data egress charges when data leaves the data center.
Configure the data format
The data is stored in text files in Azure blob storage, and each field is separated with a delimiter. In SSMS, run the following CREATE EXTERNAL FILE FORMAT command to specify the format of the data in the text files. The Contoso data is uncompressed and pipe delimited.
CREATE EXTERNAL FILE FORMAT TextFileFormat
WITH
( FORMAT_TYPE = DELIMITEDTEXT
, FORMAT_OPTIONS ( FIELD_TERMINATOR = '|'
, STRING_DELIMITER = ''
, DATE_FORMAT = 'yyyy-MM-dd HH:mm:ss.fff'
, USE_TYPE_DEFAULT = FALSE
)
);
Create the schema for the external tables
Now that you've specified the data source and file format, you're ready to create the schema for the external tables.
To create a place to store the Contoso data in your database, create a schema.
CREATE SCHEMA [asb]
GO
Create the external tables
Run the following script to create the DimProduct and FactOnlineSales external tables. All you're doing here is defining column names and data types, and binding them to the location and format of the Azure blob storage files. The definition is stored in the data warehouse and the data is still in the Azure Storage Blob.
The LOCATION parameter is the folder under the root folder in the Azure Storage Blob. Each table is in a different folder.
--DimProduct
CREATE EXTERNAL TABLE [asb].DimProduct (
[ProductKey] [int] NOT NULL,
[ProductLabel] [nvarchar](255) NULL,
[ProductName] [nvarchar](500) NULL,
[ProductDescription] [nvarchar](400) NULL,
[ProductSubcategoryKey] [int] NULL,
[Manufacturer] [nvarchar](50) NULL,
[BrandName] [nvarchar](50) NULL,
[ClassID] [nvarchar](10) NULL,
[ClassName] [nvarchar](20) NULL,
[StyleID] [nvarchar](10) NULL,
[StyleName] [nvarchar](20) NULL,
[ColorID] [nvarchar](10) NULL,
[ColorName] [nvarchar](20) NOT NULL,
[Size] [nvarchar](50) NULL,
[SizeRange] [nvarchar](50) NULL,
[SizeUnitMeasureID] [nvarchar](20) NULL,
[Weight] [float] NULL,
[WeightUnitMeasureID] [nvarchar](20) NULL,
[UnitOfMeasureID] [nvarchar](10) NULL,
[UnitOfMeasureName] [nvarchar](40) NULL,
[StockTypeID] [nvarchar](10) NULL,
[StockTypeName] [nvarchar](40) NULL,
[UnitCost] [money] NULL,
[UnitPrice] [money] NULL,
[AvailableForSaleDate] [datetime] NULL,
[StopSaleDate] [datetime] NULL,
[Status] [nvarchar](7) NULL,
[ImageURL] [nvarchar](150) NULL,
[ProductURL] [nvarchar](150) NULL,
[ETLLoadID] [int] NULL,
[LoadDate] [datetime] NULL,
[UpdateDate] [datetime] NULL
)
WITH
(
LOCATION='/DimProduct/'
, DATA_SOURCE = AzureStorage_west_public
, FILE_FORMAT = TextFileFormat
, REJECT_TYPE = VALUE
, REJECT_VALUE = 0
)
;
--FactOnlineSales
CREATE EXTERNAL TABLE [asb].FactOnlineSales
(
[OnlineSalesKey] [int] NOT NULL,
[DateKey] [datetime] NOT NULL,
[StoreKey] [int] NOT NULL,
[ProductKey] [int] NOT NULL,
[PromotionKey] [int] NOT NULL,
[CurrencyKey] [int] NOT NULL,
[CustomerKey] [int] NOT NULL,
[SalesOrderNumber] [nvarchar](20) NOT NULL,
[SalesOrderLineNumber] [int] NULL,
[SalesQuantity] [int] NOT NULL,
[SalesAmount] [money] NOT NULL,
[ReturnQuantity] [int] NOT NULL,
[ReturnAmount] [money] NULL,
[DiscountQuantity] [int] NULL,
[DiscountAmount] [money] NULL,
[TotalCost] [money] NOT NULL,
[UnitCost] [money] NULL,
[UnitPrice] [money] NULL,
[ETLLoadID] [int] NULL,
[LoadDate] [datetime] NULL,
[UpdateDate] [datetime] NULL
)
WITH
(
LOCATION='/FactOnlineSales/'
, DATA_SOURCE = AzureStorage_west_public
, FILE_FORMAT = TextFileFormat
, REJECT_TYPE = VALUE
, REJECT_VALUE = 0
)
;
Load the data
There are different ways to access external data. You can query data directly from the external tables, load the data into new tables in the data warehouse, or add external data to existing data warehouse tables.
Create a new schema
CTAS creates a new table that contains data. First, create a schema for the contoso data.
CREATE SCHEMA [cso]
GO
Load the data into new tables
To load data from Azure blob storage into the data warehouse table, use the CREATE TABLE AS SELECT (Transact-SQL) statement. Loading with CTAS leverages the strongly typed external tables you've created. To load the data into new tables, use one CTAS statement per table.
CTAS creates a new table and populates it with the results of a select statement. CTAS defines the new table to have the same columns and data types as the results of the select statement. If you select all the columns from an external table, the new table will be a replica of the columns and data types in the external table.
In this example, we create both the dimension and the fact table as hash distributed tables.
SELECT GETDATE();
GO
CREATE TABLE [cso].[DimProduct] WITH (DISTRIBUTION = HASH([ProductKey] ) ) AS SELECT * FROM [asb].[DimProduct] OPTION (LABEL = 'CTAS : Load [cso].[DimProduct] ');
CREATE TABLE [cso].[FactOnlineSales] WITH (DISTRIBUTION = HASH([ProductKey] ) ) AS SELECT * FROM [asb].[FactOnlineSales] OPTION (LABEL = 'CTAS : Load [cso].[FactOnlineSales] ');
Track the load progress
You can track the progress of your load using dynamic management views (DMVs).
-- To see all requests
SELECT * FROM sys.dm_pdw_exec_requests;
-- To see a particular request identified by its label
SELECT * FROM sys.dm_pdw_exec_requests as r
WHERE r.[label] = 'CTAS : Load [cso].[DimProduct] '
OR r.[label] = 'CTAS : Load [cso].[FactOnlineSales] '
;
-- To track bytes and files
SELECT
r.command,
s.request_id,
r.status,
count(distinct input_name) as nbr_files,
sum(s.bytes_processed)/1024/1024/1024 as gb_processed
FROM
sys.dm_pdw_exec_requests r
inner join sys.dm_pdw_dms_external_work s
on r.request_id = s.request_id
WHERE
r.[label] = 'CTAS : Load [cso].[DimProduct] '
OR r.[label] = 'CTAS : Load [cso].[FactOnlineSales] '
GROUP BY
r.command,
s.request_id,
r.status
ORDER BY
nbr_files desc,
gb_processed desc;
Optimize columnstore compression
By default, dedicated SQL pools store the table as a clustered columnstore index. After a load completes, some of the data rows might not be compressed into the columnstore. There are different reasons why this can happen. To learn more, see manage columnstore indexes.
To optimize query performance and columnstore compression after a load, rebuild the table to force the columnstore index to compress all the rows.
SELECT GETDATE();
GO
ALTER INDEX ALL ON [cso].[DimProduct] REBUILD;
ALTER INDEX ALL ON [cso].[FactOnlineSales] REBUILD;
For more information on maintaining columnstore indexes, see the manage columnstore indexes article.
Optimize statistics
It's best to create single-column statistics immediately after a load. If you know certain columns aren't going to be in query predicates, you can skip creating statistics on those columns. If you create single-column statistics on every column, it might take a long time to rebuild all the statistics.
If you decide to create single-column statistics on every column of every table, you can use the stored procedure code sample prc_sqldw_create_stats in the statistics article.
The following example is a good starting point for creating statistics. It creates single-column statistics on each column in the dimension table, and on each joining column in the fact tables. You can always add single or multi-column statistics to other fact table columns later on.
CREATE STATISTICS [stat_cso_DimProduct_AvailableForSaleDate] ON [cso].[DimProduct]([AvailableForSaleDate]);
CREATE STATISTICS [stat_cso_DimProduct_BrandName] ON [cso].[DimProduct]([BrandName]);
CREATE STATISTICS [stat_cso_DimProduct_ClassID] ON [cso].[DimProduct]([ClassID]);
CREATE STATISTICS [stat_cso_DimProduct_ClassName] ON [cso].[DimProduct]([ClassName]);
CREATE STATISTICS [stat_cso_DimProduct_ColorID] ON [cso].[DimProduct]([ColorID]);
CREATE STATISTICS [stat_cso_DimProduct_ColorName] ON [cso].[DimProduct]([ColorName]);
CREATE STATISTICS [stat_cso_DimProduct_ETLLoadID] ON [cso].[DimProduct]([ETLLoadID]);
CREATE STATISTICS [stat_cso_DimProduct_ImageURL] ON [cso].[DimProduct]([ImageURL]);
CREATE STATISTICS [stat_cso_DimProduct_LoadDate] ON [cso].[DimProduct]([LoadDate]);
CREATE STATISTICS [stat_cso_DimProduct_Manufacturer] ON [cso].[DimProduct]([Manufacturer]);
CREATE STATISTICS [stat_cso_DimProduct_ProductDescription] ON [cso].[DimProduct]([ProductDescription]);
CREATE STATISTICS [stat_cso_DimProduct_ProductKey] ON [cso].[DimProduct]([ProductKey]);
CREATE STATISTICS [stat_cso_DimProduct_ProductLabel] ON [cso].[DimProduct]([ProductLabel]);
CREATE STATISTICS [stat_cso_DimProduct_ProductName] ON [cso].[DimProduct]([ProductName]);
CREATE STATISTICS [stat_cso_DimProduct_ProductSubcategoryKey] ON [cso].[DimProduct]([ProductSubcategoryKey]);
CREATE STATISTICS [stat_cso_DimProduct_ProductURL] ON [cso].[DimProduct]([ProductURL]);
CREATE STATISTICS [stat_cso_DimProduct_Size] ON [cso].[DimProduct]([Size]);
CREATE STATISTICS [stat_cso_DimProduct_SizeRange] ON [cso].[DimProduct]([SizeRange]);
CREATE STATISTICS [stat_cso_DimProduct_SizeUnitMeasureID] ON [cso].[DimProduct]([SizeUnitMeasureID]);
CREATE STATISTICS [stat_cso_DimProduct_Status] ON [cso].[DimProduct]([Status]);
CREATE STATISTICS [stat_cso_DimProduct_StockTypeID] ON [cso].[DimProduct]([StockTypeID]);
CREATE STATISTICS [stat_cso_DimProduct_StockTypeName] ON [cso].[DimProduct]([StockTypeName]);
CREATE STATISTICS [stat_cso_DimProduct_StopSaleDate] ON [cso].[DimProduct]([StopSaleDate]);
CREATE STATISTICS [stat_cso_DimProduct_StyleID] ON [cso].[DimProduct]([StyleID]);
CREATE STATISTICS [stat_cso_DimProduct_StyleName] ON [cso].[DimProduct]([StyleName]);
CREATE STATISTICS [stat_cso_DimProduct_UnitCost] ON [cso].[DimProduct]([UnitCost]);
CREATE STATISTICS [stat_cso_DimProduct_UnitOfMeasureID] ON [cso].[DimProduct]([UnitOfMeasureID]);
CREATE STATISTICS [stat_cso_DimProduct_UnitOfMeasureName] ON [cso].[DimProduct]([UnitOfMeasureName]);
CREATE STATISTICS [stat_cso_DimProduct_UnitPrice] ON [cso].[DimProduct]([UnitPrice]);
CREATE STATISTICS [stat_cso_DimProduct_UpdateDate] ON [cso].[DimProduct]([UpdateDate]);
CREATE STATISTICS [stat_cso_DimProduct_Weight] ON [cso].[DimProduct]([Weight]);
CREATE STATISTICS [stat_cso_DimProduct_WeightUnitMeasureID] ON [cso].[DimProduct]([WeightUnitMeasureID]);
CREATE STATISTICS [stat_cso_FactOnlineSales_CurrencyKey] ON [cso].[FactOnlineSales]([CurrencyKey]);
CREATE STATISTICS [stat_cso_FactOnlineSales_CustomerKey] ON [cso].[FactOnlineSales]([CustomerKey]);
CREATE STATISTICS [stat_cso_FactOnlineSales_DateKey] ON [cso].[FactOnlineSales]([DateKey]);
CREATE STATISTICS [stat_cso_FactOnlineSales_OnlineSalesKey] ON [cso].[FactOnlineSales]([OnlineSalesKey]);
CREATE STATISTICS [stat_cso_FactOnlineSales_ProductKey] ON [cso].[FactOnlineSales]([ProductKey]);
CREATE STATISTICS [stat_cso_FactOnlineSales_PromotionKey] ON [cso].[FactOnlineSales]([PromotionKey]);
CREATE STATISTICS [stat_cso_FactOnlineSales_StoreKey] ON [cso].[FactOnlineSales]([StoreKey]);
Achievement unlocked!
You have successfully loaded public data into your data warehouse. Great job!
You can now start querying the tables to explore your data. Run the following query to find out total sales per brand:
SELECT SUM(f.[SalesAmount]) AS [sales_by_brand_amount]
, p.[BrandName]
FROM [cso].[FactOnlineSales] AS f
JOIN [cso].[DimProduct] AS p ON f.[ProductKey] = p.[ProductKey]
GROUP BY p.[BrandName]
Next steps
To load the full data set, run the example load the full Contoso retail data warehouse from the Microsoft SQL Server samples repository. For more development tips, see Design decisions and coding techniques for data warehouses.