Azure SQL is a cloud-based relational database that has been built for improved scalability. The database’s speed, ease of use, and low downtime make it a popular option among enterprises dealing with large volumes of data. Hence, Centerprise has developed a built-in Azure SQL database connector.
Users can connect to the Azure SQL database and integrate their data with multiple cloud platforms, databases, and data warehouses to improve their decision-making abilities. Astera Centerprise also features out-of-the-box transformations, including Aggregate, Filter, and Lookup transformations that can make your data analysis-ready in no time.
Connect to Azure SQL Database with Astera Centerprise
Astera Centerprise’s Azure SQL connector allows seamless code-free connectivity to the cloud database. With Astera Centerprise’s drag-and-drop designer, you can read and write Azure SQL data, define the schema for your database tables, and use custom expressions to clean, validate, and standardize your data to gain more insight and make quicker decisions.
Astera Centerprise also gives users the freedom to delete, insert, update, or upsert data in their Azure SQL tables to maintain integrity and reduce errors or inconsistencies. With a parallel-processing engine and intuitive visual UI, Astera Centeprise can help enterprises perform ETL using azure SQL connector and create efficient data pipelines for any integration project regardless of volume.
Reduce Time to Value by Automating Azure SQL Data Extraction
The screenshot below illustrates how Astera Centerprise’s connector can be used to read data from an Azure SQL database.
A dataflow showing data from an Azure SQL table being filtered
Here, we have connected to an Azure SQL database table called Sales Order Detail, filtered out data using a Filter transformation, and loaded this transformed data to an SQL Server table called Discounted Items.
Drag a Database Table Source object from the Toolbox and drop it on to the Designer. Next, select SQL Server as the Data Provider and put in the credentials to access your Azure SQL database.
Configuring an Azure SQL source
Once the connection to the database is established, pick the required source table from the drop-down list. Here, you can also select the read strategy for your table. For this example, we selected Full Load as our read strategy instead of Incremental Load to ensure that all of the data from the source table is extracted in one go.
Picking the Azure SQL table from which data needs to be extracted
Next, the Layout Builder screen appears with details about the table’s schema showing the datatypes, DB types, lengths of each field. You can also view information about the primary key for the table and see whether null values are allowed in any of the fields.
The layout of the source table with information on data types and maximum lengths of each field
Once the Database Table Source object has been configured completely, you can apply transformations to modify or enrich your data according to business rules.
Drag and drop the transformation of your choice from the Toolbox and map the fields from the Database Table Source to the transformation. Since we only wanted to load records with discounted prices in our final Database Table Destination, we have used a Filter transformation.
In the properties of the Filter transformation, we entered a custom expression to ensure that any records that do not meet our criteria are filtered out.
Properties of the Filter transformation
You can then map the output of the transformation object to more transformations or a destination object, depending on your use case.
Load Data to Azure SQL to Benefit from High Availability and Scalability
Connecting to an Azure SQL database table to load data is equally easy. All you need to do is configure the source object, apply transformations if needed, and map it to your Azure SQL table.
Using Microsoft Azure SQL as a destination
We are routing sales data from an Excel sheet and loading it onto two different Azure SQL database tables in the image above.
Two different rules are specified in the properties of the Route transformation to differentiate payments made in cash and payments made using credit or debit cards. The Route transformation has two outputs for each of these rules and a Default output in case unchanged records are needed for further processing.
Properties of the Route transformation
For both destination tables, i.e., Zone1CashPayment and Zone1CardPayment, the Azure SQL database table is configured with SQL Server as the Data Provider.
Configuring the Microsoft Azure SQL destination table
Mapping the records to the destination tables completes the Microsoft Azure integration for this example.
Reduce Time to Value of Your Enterprise Data with Bidirectional Connectivity to Azure SQL Database
Astera Centerprise’s out-of-the-box transformations and visual interface allow users to make efficient data pipelines to the cloud in no time. Automate Azure integration, consolidate data from heterogeneous sources, maintain an up-to-date database, and get analysis-ready data to improve business process efficiency.
