A nice feature with Azure Data Factory is the ability to copy multiple tables with a minimum of coding. To do this we can use a lookup, a for each loop, and a copy task.
To make this sample work you need to create all the tables you want to copy in the sink database. And make sure that you can insert values to all of the columns.
If you have primary key columns with auto-increment it needs to be changed. If you have foreign keys in your tables these need to be dropped. And all computed columns must be changed
In my lookup, I will use this code to list all the tables and schema names in the AdventureWorksLT database.
SELECT '[' + TABLE_SCHEMA + '].[' + TABLE_NAME + ']' As MyTableWithSchema
, TABLE_SCHEMA As MySchema,
TABLE_NAME As MyTable
WHERE TABLE_TYPE = 'BASE TABLE'
This gives me a list of tables and schema names inside the database
One thing that I really like with Data Factory is the possibility to execute a Function App. A Function App is really flexible and can be used to extend the available functionality in Data Factory a lot. You could, for example, process your SSAS Tabular models, do advanced file handling or send emails.
Below are some easy steps on how to execute a Function App within Data Factory.
Search for “Function App” in the search box
Azure Data Factory integrates very well with both GitHub and Azure DevOps. If you have multiple developers working in the same factory, you can even merge changes from different branches. Try doing that on an SSIS package!
Okay, so how do we start?
Setting up integration with GitHub can be done when you create your new Data Factory or you can set it up after it is created.
In this sample, I will show how to integrate with GitHub after the Data Factory is created.
In this post, I will show you how to get started with Azure Data Factory. We will use the sample data from the AdventureWorksLT database. Please read this post on how to get access to it.
First, we will start by creating a table. I am creating this table in my AdventureWorksLT database for simplicity
CREATE TABLE dbo.FactSales
FactSalesId int NOT NULL IDENTITY (1, 1),
OrderDate date NULL,
DueDate date NULL,
ShipDate date NULL,
OrderQty smallint NULL,
UnitPrice money NULL,
ProductId int NULL,
ProductName nvarchar(50) NULL
) ON [PRIMARY]
Then we will open a browser and navigate to portal.azure.com. In the search box write “Data factories” and click on it
This article will describe how to get access to sample data in Azure SQL Server. This database will be used later in other articles about Azure Data Factory.
First, log on to Azure Portal. Search for SQL. And then choose “SQL databases”
Click on “Add” to create a new database
This post describes an easy way of importing a CSV-file saved in Amazon S3 to a table in Amazon Aurora.
Technologies used in this post:
- Amazon Aurora is a cloud-based relation database which is compatible with both MySQL and PostgreSQL
- Amazon S3 is a cloud-based object storage
To get started we need to download a sample CSV file. I this case I will use this dataset: https://www.stats.govt.nz/assets/Uploads/Births-by-statistical-area-2-and-area-unit-for-comparison.csv
First, you need to upload this file to an S3 Bucket
Every once in a while I come across some “hidden” and nice features in SSMS. One of them is really nice if you want to print all columns names of a table.
So instead of writing all the column names by hand you could follow these easy steps.
– Open SSMS
– Expand you database
– Expand your table
And then drag the folder called “Columns” to your query window