Some of the most widely used cloud-based data warehouses include Amazon Redshift and Azure Synapse Analytics.

  1. Both of them provide storage analytics services to help businesses make informed decisions. However, there are key differences between Amazon Redshift and Azure Synapse Analytics.
  2. For many years, data warehouses have been crucial for enterprise analytics and reporting. However, traditional data warehouses were not designed to cope with the rapid growth of data.
  3. As a result, it fails to handle the constantly evolving needs of end-users. Although traditional data warehouses are effective, cloud-based data warehouses offer additional benefits like flexibility and scalability. 
  4. As a result, cloud-based data warehouses have emerged as a popular solution for storing, reporting, and analyzing data in a secure and cost-effective manner. 

Key Feature of Amazon Redshift

  1. Powerful data warehousing.
  2. Direct integration with various AWS services.
  3. Automatic concurrency scaling for optimal query performance.
  4. Serverless option for cost-effective scaling.
  5. Streaming data ingestion for real-time data analysis.
  6. High reliability and uptime with automated backups and maintenance.
  7. Advanced security, access control, and compliance.
Amazon Redshift vs Azure Synapse: Amazon Redshift Architecture
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Azure Synapse Overview

amazon redshift vs azure synapse: azure synapse analysis
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Azure Synapse Analytics, This platform offers two types of computing environments to support different workloads: 

  • SQL compute environment known as SQL pool
  • Spark compute environment known as Spark pool

Azure Synapse provides a unified portal called Synapse Studio. It creates a workspace for data preparation, data management, data exploration, and data warehousing. You can choose a compute environment that best suits your business requirements.

Here are the key features of Azure Synapse:

  1. Provides limitless scalability for big data analytics.
  2. Generate powerful insights using machine learning on transformed data.
  3. Provides unified experience for end-to-end analytics solutions.
  4. Offers advanced security and privacy features.
  5. Easy integration with other Microsoft Azure services.

Comparison Factors – Amazon Redshift vs Azure Synapse 

Comparison FactorsAmazon RedshiftAzure Synapse 
Data Analytics CapabilitiesLacks built-in analytics Capabilities but can integrate with AWS services like Amazon EMR, and QuickSight for advanced analytics.Comprehensive analytics service with built-in advanced analytics capabilities, including Apache Spark-based analytics, machine learning, graph analysis, and predictive analytics.
Data IntegrationSupports direct integration with several AWS services and select AWS partners.Has 95+ pre-built connectors to various data sources.
Support for Machine LearningHas limited Built-in machine learning capabilities.
Integration with Amazon SageMaker for advanced machine learning tasks.
Has Advanced Built-in machine learning capabilities.
Supported by Azure Machine Learning.
Availability and Disaster RecoverySupports manual and automated snapshots of clusters.
The Retention period for snapshots is customizable.
Supports manual and automated snapshots of clusters.
The Retention period for snapshots is 7-35 days.
Data Security and Access ControlIndirectly supports OAuth2  by integrating with other AWS services.
Permissions are applied to entire tables.
Directly supports OAuth2.
Granular permissions on schemas, tables, views, individual columns, procedures, and other objects. 
PricingPricing is determined by node type, number of nodes, usage duration, data transfer, and snapshots.Pricing is based on data storage, data processing, and SQL pool provisioning.

Azure Synapse Analytics vs AWS Redshift: Which is Better?

  1. Choosing between Redshift and Azure Synapse Analytics can be challenging for businesses looking to adopt a cloud data warehousing solution. Both platforms offer unique features and capabilities that can address specific business needs.
  2. Amazon Redshift is suitable for small to large-scale businesses that require a scalable and cost-effective data warehousing solution. It is easy to set up, maintain, and integrate well with other AWS services, making it ideal for AWS users. 
  3. Amazon Redshift is an ideal choice if you want a data warehousing solution that can handle massive amounts of data and requires minimal maintenance.
  4. On the other hand, Synapse is well-suited for organizations that require an integrated analytics solution that can handle both data warehousing and big data analytics.
  5. It has built-in advanced analytics capabilities and can handle large volumes of data efficiently. 
  6. Azure Synapse is ideal for users who require a highly scalable data warehousing solution that can handle complex data structures and provide real-time insights.
  7. Synapse Analytics seamlessly integrates with other Azure services, making it an all-in-one solution for businesses’ analytics requirements. 
  8. Synapse Analytics interface includes a drag-and-drop feature that allows users to visually build and design data flows to transform, aggregate, and prepare data for analysis. 
  9. The no-code capabilities of Synapse empower even non-technical users to gain insights from their data and make data-driven decisions.

Learn more about Amazon Redshift Vs Hadoop: How to Make the Right Choice?


  1. Amazon Redshift and Azure Synapse Analytics are powerful cloud-based data warehousing solutions offering unique features and capabilities. 
  2. Redshift is a better choice for data warehousing use cases.And Synapse is a better choice for integrated analytics use cases that require both data warehousing and big data analytics capabilities.
  3. Synapse Analytics seamlessly integrates with other Azure services, offering an end-to-end analytics solution for businesses. 
  4. However, if data warehousing is the primary focus, Redshift is a strong contender with its ease of use and scalability. Ultimately, the choice between the two will depend on the business’s specific requirements.
  5. In case you want to integrate data into your desired Database/destination, then Hevo Data is the right choice for you! It will help simplify the ETL and management process of both the data sources and the data destinations. 

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Amulya Reddy
Technical Content Writer, Hevo Data

Amulya combines her passion for data science with her interest in writing on various topics related to data, software architecture, and integration. She excels in leveraging advanced data analytics, ETL processes, and machine learning algorithms to provide insightful and comprehensive content. Amulya’s unique ability to transform complex data into actionable insights sets her apart, driving innovation and understanding in the tech community.

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