Integrating APIs with SQL Server streamlines data flow and enhances the functionality and versatility of SQL Server, providing a dynamic platform for real-time data updates and interactions.

Upon a complete walkthrough of this blog, you can successfully set up SQL Server REST API Integration and seamlessly transfer your data for a fruitful analysis.

Follow our step-by-step guide to efficiently transfer your data from REST APIs to Microsoft SQL Server.

It will further help you build a customized ETL pipeline for your organization.

Methods to Set Up SQL Server REST API Integration

There are multiple ways in which you can set up the SQL Server REST API Integration:

Method 1: Using Hevo Data to Set Up SQL Server REST API Integration

Hevo Data, the only No-code Data Pipeline, helps you transfer data from REST APIs to Microsoft SQL Server & lets you visualize it in a BI tool.

Hevo is fully managed and completely automates not only loading data from 150+ data Sources but also enriching the data and transforming it into an analysis-ready form without writing a single line of code.

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Hevo Data focuses on two simple steps to help you connect REST API to SQL database:

  • Configure Source: Connect Hevo Data with your REST API source by providing a unique name for your Data pipeline and information about the method you want to use, choosing between GET and POST. You will also need to provide the URL for your API endpoint, the data root for your API, and your credentials, such as username and password, to allow Hevo to access your data, along with information about your query parameters and API headers.
SQLserver REST API - Configuring REST API as Source: sql server rest api
  • Integrate Data: Load data from REST APIs to Microsoft SQL Server by providing a unique name for your destination and your database credentials, such as username and password. To help Hevo connect with your Microsoft SQL Server database, you will also have to provide information such as the host IP, port number, and the name & schema of your database.
SQLserver REST API - Configuring MS SQL Server as Destination: sql server rest api

With this, you have successfully set up SQL Server REST API Integration.


Method 2: Using Microsoft SQL Server Integration Services (SSIS) to Set Up SQL Server REST API Integration

The Microsoft SQL SSIS (Server Integration Services) is a robust component and functionality of the Microsoft SQL Server Database that allows users to carry out numerous complex data migration tasks easily.

It also supports connecting with REST API-based sources and lets users ingest data into their Microsoft SQL Server databases.

You can set up the SQL Server REST API Integration by leveraging the connection manager using the following steps:

Step 1: Defining a Data Flow Task

SQLserver REST API - Defining a Data Flow Task: SQL Server REST API Integration.
Image Source: Self

To start setting up the SQL Server REST API Integration, you must first define a new data flow-based task. You must specify the Microsoft SQL Server ADO.Net destination and new REST source.

To do this, go to the connection manager, add a new connection, and choose the connection type REST by selecting the REST option from the dropdown list.

This is how you can define a data flow task.

Step 2: Configuring Connection Properties

With your task now added, you need to configure the properties for the connection. To do this, you will have to specify the connection properties such as the REST URI, your credentials, and the authentication format, choosing between OAuth, HTTP, NTLM, and Digest, along with information about the incoming file format like JSON, XML, etc.

SQLserver REST API - Configuring Data Mapping and Destination Properties: sql server rest api
Image Source: Self

Once you’ve provided the necessary information, you need to specify the “DataModel” property that helps map incoming data into database tables.

You can choose between the following options for the Data Model property:

  • Relational: This helps ensure that mapping for incoming data takes place in a way that maintains referential integrity.
  • Document: This helps map data into nested or hierarchical data documents.
  • Flattened Documents: This helps aggregate data from nested documents and their parent documents into a single table.

This is how you can configure the connection properties.

Step 3: Configuring Extraction Properties of REST Data Source

SQLserver REST API - Configuring the Extraction Properties for REST Sources: sql server rest api
Image Source: Self

With your connection properties now configured, you must configure the REST source extraction properties.

To do this, you must have the “Create Global Objects” permission and provide the query that will help extract data from your data source.

Once you’ve done that, close the REST source and connect it to the ADO.NET-based destination.

Step 4: Configuring the Microsoft SQL Server Database

With all the necessary configurations now done, you now need to configure your Microsoft SQL Server destination database.

You can do this by providing the database name, tables, and information about the desired data access mode and choosing between views/tables.

Now, map the incoming data with the destination column and then click on the advanced option and specify the following properties:

  • BulkInsertFireTriggers: Should bulk insert fire triggers on destination tables.
  • BulkInsertOrder: Sort columns specified in ascending/descending order.
  • MaxInsertCommitSize: Maximum number of rows to insert in a single batch) 
  • DefaultCodePage: Code page to use if the source does not provide one.

You can now execute this workflow to load data from REST API to Microsoft SQL Server databases.

This is how you can set up the SQL Server REST API integration using the Microsoft SQL Server Integration Service (SSIS).

Limitations of Using Microsoft SQL Server Integration Services (SSIS)

Some of the limitations of using Microsoft SQL Server Integration Services (SSIS) for SQL Server REST API Integration include:

  • The Microsoft SQL Server Integration Service (SSIS) only supports the Microsoft SQL Server database. Hence, if your business data needs require using a diverse set of databases, then using SSIS will not work.
  • If your systems require you to delegate data storage or processing to a particular tool based on the data type, such as MongoDB storage or Neo4J for graph databases, then SSIS can limit your options.

Check out how Google Cloud Rest API Integration effortlessly transfer data for comprehensive analysis.

Method 3: Using Custom Code Snippets to Set Up SQL Server REST API Integration

To start loading data from REST APIs, you must first leverage the JDBC driver’s “DriverManager” class to obtain and establish a connection with your Microsoft SQL Server instance.

You can use the following syntax for creating your connection URL:


Once you’ve set up the necessary configurations and created your connection URL, you must create a statement object that will carry out SQL operations such as insert, delete, update, etc., and fetch results.

With your statement object ready, you must execute the insert command in a repetitive loop based on conditional logic.

Once you’ve executed the insert statement or any other operation, you must close the statement and connection object.

For example, if you want to insert, set, and update values in your Microsoft SQL Server database using APIs, you can do so using the following lines of code:

While (someCondition) {
// Specify the connection URL as per SQL server connection string.
        String connectionUrl = "jdbc:sqlserver://<YourServer>:<port>;databaseName=HevoMSSqlTest;user=<user>;password=<password>";

        try {
//LOAD the API driver 	
//Get a new Connection to SQL Server 
	Connection conn = DriverManager.getConnection(connectionUrl); 

// Create a statement object, this is the vehicle for carrying your SQL inserts				PreparedStatement stmt = conn.prepareStatement("INSERT into my_records" +" VALUES(?,?,?,?,?)");

// SET the values in your table columns 							
	stmt.setInt(1, 0);			
	stmt.setString(2, myString);					
	stmt.setString(4, myName);					
	stmt.setString(5, myRole);

//EXECUTE the update 					
	}// End of TRY block 
        catch (Exception e) {
        System.err.println("Error: ");

	finally {
// CLOSE the Statement and Connection objects 
} // END of While loop 

This is how you can develop custom code snippets that leverage the JDBC driver for Microsoft SQL Server to set up the SQL Server REST API Integration.

Limitations of Using Custom Code Snippets

Though you can efficiently set up SQL Server REST API Integration manually, several limitations are associated with this method. Some of the limitations include:

  • Using drivers requires keeping track of updates and manually updating them when new releases are available or when your technology stack(Java, Node.js, C++, Python) undergoes updates. Similarly, existing API calls and methods may depreciate with new versions and require upgrades.
  • To ensure smooth data transfers and high efficiencies, you must add/remove/change the new properties associated with your connections when any change occurs.
  • Working with APIs to load data requires strong technical knowledge to handle operations such as connection pooling, query optimization, compression, validation, etc.


This article explains how to quickly load data from REST API to Microsoft SQL Server by setting up the SQL Server REST API Integration.

It provides in-depth knowledge about the concepts behind every step to help you understand and implement them efficiently.

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Gain a deeper insight into SQL Server Integrations with these essential reads:

These methods, however, can be challenging, especially for a beginner & this is where Hevo Data saves the day.

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Tell us about your experience setting up the SQL Server REST API integration! Share your thoughts in the comments section below!

Pratik Dwivedi
Freelance Technical Content Writer, Hevo Data

Pratik writes about various topics related to data industry who loves creating engaging content on topics like data analytics, machine learning, AI, big data, and business intelligence.

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