Snowflake and Amazon Redshift are key competitors in the data warehousing arena. Snowflake generally has the upper hand due to its superior data accessibility and management features.
Features: Snowflake features multi-formatted data accessibility and advanced data management capabilities such as Snowpipe and zero-copy cloning. It offers seamless scaling without service interruption. Its architecture supports handling varied file formats which enhances its flexibility. Amazon Redshift provides excellent integration into the AWS ecosystem and supports columnar storage technology that optimizes read access. It offers fast query performance with massively parallel processing capabilities, making it efficient at handling large datasets. JSON format support and the ability to perform analytics on the fly are other notable features.
Room for Improvement: Snowflake could improve its geo-spatial query capabilities and develop more intuitive user interfaces for non-technical users. Transparency in pricing is another area for enhancement. Amazon Redshift needs to reduce recovery times for large datasets and enhance seamless real-time integration with other AWS services like RDS. Users also desire better query performance and support for more complex data processing functions.
Ease of Deployment and Customer Service: Both Snowflake and Amazon Redshift provide robust customer support, with Snowflake being praised for responsiveness, although it lacks standardized service level agreements. Redshift has a responsive support team and satisfactory customer feedback. Deployment is more straightforward with Snowflake, attributed to its self-managing design, whereas Redshift requires careful planning and configuration.
Pricing and ROI: Snowflake employs a flexible, credit-based pricing model, providing cost efficiency for scalable usage, though some users find its pricing less transparent. Amazon Redshift offers competitive pricing with a per-use model, which can become less economical for large-scale operations due to fixed cluster management costs. Users perceive both platforms as providing good value, but Snowflake is often preferred for efficiency and lower overall costs in large-scale scenarios.
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.
It's costly when you enable support.
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
I am satisfied with the work of technical support from Snowflake; they are responsive and helpful.
The technical support from Snowflake is very good, nice, and efficient.
The scalability part needs improvement as the sizing requires trial and error.
Snowflake is very scalable and has a dedicated team constantly improving the product.
The billing doubles with size increase, but processing does not necessarily speed up accordingly.
Recently, Snowflake has introduced streaming capabilities, real-time and dynamic tables, along with various connectors.
Amazon Redshift is a stable product, and I would rate it nine or ten out of ten for stability.
Snowflake is very stable, especially when used with AWS.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
They should bring the entire ETL data management process into Amazon Redshift.
Integration with AI could be a good improvement.
Enhancements in user experience for data observability and quality checks would be beneficial, as these tasks currently require SQL coding, which might be challenging for some users.
What things you are going with to ask the support and how we manage the relationship matters a lot.
If more connectors were brought in and more visibility features were added, particularly around cost tracking in the FinOps area, it would be beneficial.
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.
Snowflake's pricing is on the higher side.
Snowflake lacks transparency in estimating resource usage.
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.
We had a comparison with Databricks and Snowflake a few months back, and this auto-scaling takes an edge within Snowflake; that's what our observation reflects.
I have used the Snowflake Zero-Copy Cloning feature in the past while prototyping data in lower environments. This feature is helpful as it saves a lot of time during the data replication process.
Snowflake is a data lake on the cloud where all processing happens in memory, resulting in very fast query responses.
| Product | Market Share (%) |
|---|---|
| Snowflake | 16.1% |
| Amazon Redshift | 7.4% |
| Other | 76.5% |
| Company Size | Count |
|---|---|
| Small Business | 27 |
| Midsize Enterprise | 21 |
| Large Enterprise | 28 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 20 |
| Large Enterprise | 58 |
Amazon Redshift is a fully administered, petabyte-scale cloud-based data warehouse service. Users are able to begin with a minimal amount of gigabytes of data and can easily scale up to a petabyte or more as needed. This will enable them to utilize their own data to develop new intuitions on how to improve business processes and client relations.
Initially, users start to develop a data warehouse by initiating what is called an Amazon Redshift cluster or a set of nodes. Once the cluster has been provisioned, users can seamlessly upload data sets, and then begin to perform data analysis queries. Amazon Redshift delivers super-fast query performance, regardless of size, utilizing the exact SQL-based tools and BI applications that most users are already working with today.
The Amazon Redshift service performs all of the work of setting up, operating, and scaling a data warehouse. These tasks include provisioning capacity, monitoring and backing up the cluster, and applying patches and upgrades to the Amazon Redshift engine.
Amazon Redshift Functionalities
Amazon Redshift has many valuable key functionalities. Some of its most useful functionalities include:
Reviews from Real Users
“Redshift's versioning and data security are the two most critical features. When migrating into the cloud, it's vital to secure the data. The encryption and security are there.” - Kundan A., Senior Consultant at Dynamic Elements AS
“With the cloud version whenever you want to deploy, you can scale up, and down, and it has a data warehousing capability. Redshift has many features. They have enriched and elaborate documentation that is helpful.”- Aishwarya K., Solution Architect at Capgemini
Snowflake provides a modern data warehousing solution with features designed for seamless integration, scalability, and consumption-based pricing. It handles large datasets efficiently, making it a market leader for businesses migrating to the cloud.
Snowflake offers a flexible architecture that separates storage and compute resources, supporting efficient ETL jobs. Known for scalability and ease of use, it features built-in time zone conversion and robust data sharing capabilities. Its enhanced security, performance, and ability to handle semi-structured data are notable. Users suggest improvements in UI, pricing, on-premises integration, and data science functions, while calling for better transaction performance and machine learning capabilities. Users benefit from effective SQL querying, real-time analytics, and sharing options, supporting comprehensive data analysis with tools like Tableau and Power BI.
What are Snowflake's Key Features?
What Benefits Should You Look for?
In industries like finance, healthcare, and retail, Snowflake's flexible data warehousing and analytics capabilities facilitate cloud migration, streamline data storage, and allow organizations to consolidate data from multiple sources for advanced insights and AI-driven strategies. Its integration with analytics tools supports comprehensive data analysis and reporting tasks.
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