

Amazon Redshift and Microsoft Azure Synapse Analytics compete in the cloud data warehousing category. Microsoft Azure Synapse Analytics appears to have a slight edge due to its robust integration with Azure services and advanced analytics capabilities.
Features: Amazon Redshift offers robust scalability with flexible node size adjustments, enabling efficient management of large datasets. It supports massive parallel processing and fast data retrieval. Microsoft Azure Synapse Analytics provides strong parallel processing capabilities and shines with its integrated analytics functions along with seamless integration with other Azure services.
Room for Improvement: Amazon Redshift could improve in handling large snapshot restores, better AWS IAM integration for enhanced security, and real-time integration capabilities. Microsoft Azure Synapse Analytics can enhance concurrent query handling, decrease costs, and strengthen data governance and machine learning capabilities. Users also desire simplification in setup complexity and more comprehensive documentation.
Ease of Deployment and Customer Service: Amazon Redshift is commonly deployed in public cloud environments, noted for ease of use but with limited direct customer support, depending heavily on web-based support resources. Microsoft Azure Synapse Analytics allows deployment across public and hybrid clouds, backed by satisfactory customer service. However, users express concerns about configuration complexity and the absence of real-time integration.
Pricing and ROI: Amazon Redshift is appreciated for its cost-effective pricing models which suit long-term storage and usage, focusing on pricing transparency and effective cost management strategies. Azure Synapse Analytics offers a flexible pay-as-you-go model, though it can become expensive as usage scales, making some users find the solution relatively costly. Both promise high ROI within large-scale data operations, with cost management being key for maximizing investment returns.
We earned back our investment in Amazon Redshift within the first year.
Some of my customers have indeed seen a return on investment with Microsoft Azure Synapse Analytics as they used it for analytics to drive decision-making, improving their processes or increasing revenue.
Whenever we need support, if there is an issue accessing stored data due to regional data center problems, the Amazon team is very helpful and provides optimal solutions quickly.
Documentation that allows anyone with prior knowledge of Redshift or SQL to resolve technical issues.
It's costly when you enable support.
They are slow to respond and not very knowledgeable.
This is an underestimation of the real impact because we use big data also to monitor the network and the customer.
I would rate the support for Microsoft Azure Synapse Analytics as an eight out of ten.
The scalability part needs improvement as the sizing requires trial and error.
We have successfully increased our storage space, which was a smooth process without server crashes before or after scaling.
Microsoft Azure Synapse Analytics is scalable, offering numerous opportunities for scalability.
For the scalability of Microsoft Azure Synapse Analytics, I would rate it a 10 until you remain in the Azure Cloud scalability framework.
Recovering from such scenarios becomes a bit problematic or time-consuming.
Amazon Redshift is a stable product, and I would rate it nine or ten out of ten for stability.
Performance and stability are absolutely fine because Microsoft Azure Synapse Analytics is a PaaS service.
I find the service stable as I have not encountered many issues.
We have never integrated Microsoft Azure Synapse Analytics with Databricks, but we have mostly pulled data from on-premises systems into Azure Databricks.
They should bring the entire ETL data management process into Amazon Redshift.
Integration with AI could be a good improvement.
Integration with AI features could elevate its capabilities and popularity.
Microsoft Azure Synapse Analytics is an excellent product because it includes both SIEM and orchestration capabilities with playbooks.
There is a need for better documentation, particularly for customized tasks with Microsoft Azure Synapse Analytics.
Databricks is a very rich solution, with numerous open sources and capabilities in terms of extract, transform, load, database query, and so forth.
The cost of technical support is high.
It's a pretty good price and reasonable for the product quality.
The pricing of Amazon Redshift is expensive.
The cheapest tier costs about $4,000 to $4,700 a year, while the most expensive tier can reach up to $300,000 a year.
I think the price of Microsoft Azure Synapse Analytics is very expensive, but that's not only for Microsoft Azure Synapse Analytics—it's for the cloud in general.
I find the pricing of Microsoft Azure Synapse Analytics reasonable.
Amazon Redshift's performance optimization and scalability are quite helpful, providing functionalities such as scaling up and down.
Scalability is also a strong point; I can scale it however I want without any limitations.
The specific features of Amazon Redshift that are beneficial for handling large data sets include fast retrieval due to cloud services and scalability, which allows us to retrieve data quickly.
One of the most valuable features in Microsoft Azure Synapse Analytics is the ability to write your own ETL code using Azure Data Factory, which is a component within Synapse.
Microsoft Azure Synapse Analytics offers significant visibility, which helps us understand our usage more clearly.
For Microsoft Azure Synapse Analytics, the integration is the most valuable feature, meaning that whatever you need is fast and easy to use.
| Product | Mindshare (%) |
|---|---|
| Amazon Redshift | 7.0% |
| Microsoft Azure Synapse Analytics | 5.6% |
| Other | 87.4% |


| Company Size | Count |
|---|---|
| Small Business | 27 |
| Midsize Enterprise | 21 |
| Large Enterprise | 29 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 18 |
| Large Enterprise | 56 |
Amazon Redshift is a dynamic data warehousing and analytics platform offering scalability and seamless AWS integration for high-performance query processing and diverse data management.
Amazon Redshift provides robust data integration capabilities with AWS services like S3 and QuickSight, enabling efficient data warehousing and analytics. It is known for fast query performance due to its columnar storage and can handle diverse file formats. With a user-friendly SQL interface, Redshift supports data compression and offers a strong cost-performance ratio. Its secure VPC configurations and compatibility with data science tools enhance its functionality, although there is room for improving snapshot restoration, dynamic scaling, and processing large datasets.
What are the key features of Amazon Redshift?In industries, Amazon Redshift is essential for managing extensive datasets for business intelligence, operational insights, and reporting. It supports data integration from ERPs and S3, handles SQL queries for comprehensive analysis, and facilitates data storage and transformation. Companies use it for predictive modeling and connect with BI tools like Tableau and Power BI to derive actionable insights.
Microsoft Azure Synapse Analytics integrates data warehousing and big data analytics seamlessly. It provides scalability and user-friendly features for efficient, real-time reporting and data management.
Azure Synapse Analytics is designed for seamless data integration, allowing users to scale their operations effectively while providing extensive analytics capabilities. It supports both traditional data warehousing and big data solutions with real-time reporting through an interactive interface that integrates well with Power BI. The platform's serverless flexibility optimizes cost while ensuring robust security, leveraging users' familiarity with SQL technologies. Scalability allows processing of large datasets efficiently, empowering companies to connect disparate data sources and support industry-specific needs. Despite its strengths, Synapse users often seek improved governance, schema management, and technical support. Enhanced integration with Microsoft and third-party tools, along with better data loading capabilities, are also desired.
What are the key features of Microsoft Azure Synapse Analytics?Azure Synapse Analytics is extensively implemented across sectors like healthcare, finance, marketing, and government. Organizations use it to build data pipelines, perform analytics modeling, and facilitate reporting. It supports data transformation, migration, and orchestration, enhancing business intelligence and decision-making capabilities by efficiently handling big data and connecting disparate data sources.
We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.