Azure SQL is a database service that facilitates high-performance querying with complete infrastructure management. However, it does not provide clarity on database transaction unit requirements and does not support connections with linked servers. All these limitations make Azure SQL a less preferred database solution.

To overcome these challenges, you can integrate it with database services like Redshift, which supports linked server connection with servers like SQL. Redshift is a data warehouse offering massively parallel processing and columnar storage facilities with high scalability at the petabyte level. You can integrate Azure SQL to Redshift to conduct advanced data processing and gain useful insights to improve your organization’s growth. 

Are you wondering how do I migrate data from Azure SQL to Redshift? This article explains two methods for transferring data from Azure SQL to Redshift. 

Why Integrate Azure SQL to Redshift

The following reasons indicate it would be beneficial if you import data into Redshift from Azure SQL, such as:

  • You can easily integrate Redshift with other AWS services to leverage its best performance and security features. Azure SQL, which is managed by Microsoft, may not be easily integrated with AWS services. 
  • Redshift provides massively parallel processing capabilities that allow you to query your datasets faster than that in Azure SQL. 
  • Azure SQL is a cost-effective option for small enterprises with a smaller volume of datasets. In contrast, Redshift is a cost-effective solution for large enterprises with a high volume of data. 
  • Redshift is a more suitable service for online analytical processing (OLAP), while Azure SQL is an online transactional processing (OLTP) database. 
  • You can store your data in a columnar format in Redshift, enabling you to store and perform complex data analytics on large volumes of datasets. Thus, you can convert Azure SQL to Redshift to handle massive datasets efficiently. 

Azure SQL Overview

Azure SQL is a PaaS (Platform as a Service) database solution offered by Microsoft Azure. As a fully managed service, it handles infrastructure management tasks such as upgradation, patching, backup, and monitoring. Azure SQL also provides high availability and performance and helps you manage your data effectively for analytics. 

There are two deployment options in Azure SQL. You can use a Single Database or an Elastic Pool for Azure SQL database deployment. A single database is a fully managed, isolated database similar to the SQL Server database engine. An Elastic Pool, on the other hand, is a collection of single databases that share resources, such as CPU or memory. You can choose any deployment options according to your requirements for using Azure SQL for complex analytics. 

Redshift Overview

Redshift is a cloud-based data warehouse platform managed by Amazon Web Services (AWS). It offers a petabyte level of scalability and massively parallel processing capabilities for faster querying of large amounts of data. You can also conduct predictive analytics with Redshift ML, which helps you to build and execute machine learning models within the data warehouse. 

Redshift offers robust security features, such as role-based access controls, row and column-level security, and easy authentication mechanisms to secure your data. It also provides a columnar data storage format that enhances compression and provides faster query performance. 

Methods to Integrate Azure SQL to Redshift

  • Method 1: Using CSV file to Integrate Azure SQL to Redshift
  • Method 2:  Using Hevo Data to Integrate Azure SQL to Redshift

Method 1: Using CSV file to Integrate Data from Azure SQL to Redshift

You can use CSV files to convert Azure SQL to Redshift as follows:

Step 1: Export Data from Azure SQL to a CSV file Using BCP

You can export Azure SQL data to a CSV file with the help of Bulk Copy Program (BCP) by following the below-mentioned steps:

  • First, ensure that the BCP utility is installed in your system. For this, you can run the following command in the Command Prompt:
C:\WINDOWS\system32> bcp /v

The output will be the following if BCP is installed:

C:\WINDOWS\system32> bcp /v
BCP - Bulk Copy Program for Microsoft SQL Server.
Copyright (C) Microsoft Corporation. All Rights Reserved.
Version: 15.0.2000.5

After this, run the following code to export your desired data table to a CSV file:

bcp table_name out C:\Path\target_file_name -c -U username -S

Step 2: Uploading CSV file to S3 Bucket

Follow these steps to upload a CSV file in your native system to the Amazon S3 bucket.

Create a Bucket
  • First, sign in and go to the Amazon S3 Console in the AWS Management Console.
  • Next, choose Create Bucket and the AWS region where you want to create it. 
  • Now, enter a unique bucket name in the Bucket Name option of the Create Bucket dialog box.
  • Choose the suggested defaults for the rest of the options, and then choose Create Bucket.
  • On successfully creating an Amazon S3 bucket, the Console displays your empty bucket in the Buckets panel.
Create a Folder
  • First, choose the name of the new bucket.
  • Then, click on the Create Folder button.
  • Name the new folder as load.
Upload the CSV files to the new Amazon S3 bucket
  • Choose the name of the data folder where you want to upload the CSV file. 
  • In the Upload wizard, choose Add Files and follow the Amazon S3 Console instructions to upload your CSV file. 
  • Choose Upload

Step 3: Exporting Data from S3 Bucket to Redshift

After transferring the CSV file to the S3 bucket, you should use the COPY command to export the data in it to Redshift using the following code:

COPY table_name [ column_list ] FROM data_source CREDENTIALS access_credentials [options] 

Limitations of Using CSV File to Load Data from Azure SQL to Redshift

There are certain limitations of using a CSV file and S3 bucket to copy data from Azure SQL to Redshift, such as:

  • Limited features of CSV: CSV files support basic data types such as numbers or dates but do not support complex data types like nested objects or images. Also, they do not provide data security mechanisms like access control or encryption, which may expose your data to security threats. 
  • Time-consuming: Redshift is suitable for handling large volumes of data. CSV files present certain constraints when dealing with massive amounts of data and may take a lot of time to transfer this data. This makes CSV files a less preferred option for data migration from Azure SQL to Redshift. 
  • Constraints of S3 bucket: It is not possible to transfer the ownership of the S3 bucket to an AWS account other than the one in which it was created. Also, you cannot change the name and AWS region of the S3 bucket after the initial setup. This can create constraints in seamless data integration, especially for new AWS users.

Method 2: Using Hevo Data to Integrate Azure SQL to Redshift

Hevo Data is a no-code ELT platform that provides real-time data integration and a cost-effective way to automate your data pipeline workflow. With over 150 source connectors, you can integrate datasets from multiple platforms, conduct advanced analysis on your data, and produce useful insights.

Here are some of the most important features provided by Hevo Data:

  • Data Transformation: Hevo Data allows you to transform your data for analysis with a simple Python-based drag-and-drop data transformation technique.
  • Automated Schema Mapping: Hevo Data automatically arranges the destination schema to match the incoming data. It also lets you choose between Full and Incremental Mapping.
  • Incremental Data Load: It ensures proper bandwidth utilization at both the source and the destination by allowing real-time data transfer of the modified data.

Step 1: Configuration of Azure SQL as a Source 


Once all the prerequisites are fulfilled, follow the steps mentioned here to configure Azure SQL as a source in Hevo:

  • From the Navigation Bar, click PIPELINES.
  • Click + CREATE in the Pipelines List View.
  • From the Select Source Type page, select Azure SQL Server.
  • In the Configure your Azure SQL Server Source page, specify the following:
Azure SQL to Redshift: Configure Source Settings
Azure SQL to Redshift: Configure Source Settings

For further information on the configuration of Azure SQL as a source, refer to the Hevo documentation

Step 2: Configuration of Redshift as Destination


After fulfilling all the prerequisites, follow the steps mentioned below to configure Redshift as a destination in Hevo:

  • Click DESTINATIONS on the Navigation Bar.
  • From the Destinations List View, click + CREATE.
  • From the Add Destination page, select Amazon Redshift.
  • On the Configure your Amazon Redshift Destination page, you can specify the following:
Azure SQL to Redshift: Configure Destination Settings
Azure SQL to Redshift: Configure Destination Settings

For more information on the configuration of Redshift as a destination, refer to the Hevo documentation

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Use Cases of Azure SQL to Redshift

There are several use cases of Azure SQL to Redshift integration, such as:

  • Business Intelligence: After integrating Azure SQL to Redshift, you can combine it with BI tools like Tableau, Power BI, Looker, etc., to create interactive dashboards and reports to gain useful business insights.
  • Financial Sector: Redshift can help you store and analyze financial data related to stock markets, transaction data, or risk data. 
  • Healthcare Services: You can use Redshift to streamline the organization and analysis of healthcare data, such as patient health histories or research data related to the medical sector. 


This blog provides information on how to transfer data from Azure SQL to Redshift. It gives information on two methods for data integration. One involves using a CSV file to transfer data from Azure SQL to Redshift. However, it has some drawbacks because of the limitations of CSV files, such as a lack of support for complex data types and the absence of security features. 

The other method uses Hevo Data, an automated data integration platform for this integration. It offers features such as zero-code data pipeline creation capabilities, a simple interface, and an extensive connector library for seamless data transfer. Schedule a demo to leverage these advantages of Hevo for successful data integration today!


  1. How is Azure SQL different from SQL Server?

Azure SQL is a cloud-based service that automates infrastructure management tasks such as upgrading or monitoring databases. You have to do these tasks manually while using the SQL Server. However, you can deploy SQL Server on a cloud platform like Azure Infrastructure as a Service (IaaS). It runs the SQL Server with the help of virtual machines while you have to take responsibility for infrastructure management tasks such as upgradation or monitoring the virtual machines. 

  1. What is an Amazon S3 bucket?
    Amazon S3 bucket is a data storage service that stores data as objects in buckets. In addition to storage, S3 buckets are also used for data backup and delivery. 
Customer Experience Engineer, Hevo Data

Dimple, an experienced Customer Experience Engineer, possesses four years of industry proficiency, with the most recent two years spent at Hevo. Her impactful contributions significantly contribute to refining customer experiences within the innovative data integration platform.

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