Amazon Redshift vs Snowflake comparison

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Amazon Web Services (AWS) Logo
7,785 views|5,798 comparisons
87% willing to recommend
Snowflake Computing Logo
21,234 views|11,994 comparisons
96% willing to recommend
Comparison Buyer's Guide
Executive Summary

We performed a comparison between Amazon Redshift and Snowflake based on real PeerSpot user reviews.

Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
To learn more, read our detailed Amazon Redshift vs. Snowflake Report (Updated: May 2024).
770,141 professionals have used our research since 2012.
Q&A Highlights
Question: What is the major difference between AWS Redshift and Snowflake?
Answer: Interesting. Snowflake has a fundamentally different architecture in that compute and storage are completely separated allowing you to scale each dynamically and independently This makes me to get into Snowflake, Almost I am using Snowflake last 8 months. Its awesome.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The stability of Amazon Redshift is good.""Changing from local servers to the cloud is very easy. It's so nice not to have to worry about physical servers.""The solution has very competitive pricing.""This service can merge and integrate well with all databases.""I have primarily used the Redshift Spectrum feature and found it most valuable.""In terms of valuable features, I like the columnar storage that Redshift provides. The storage is one of the key features that we're looking for. Also, the data updates and the latency between the data-refreshes.""I found the Amazon Redshift computing services easy. I found the computing instances the most incredible in the solution.""It allows you write complex queries and perform row by row processes."

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"Snowflake is a database, and it is very good and useful. The most interesting part is that memory management is very good in Snowflake. For a business intelligence project, SQL Server is taking a lot of time for reporting services. There are a lot of calculations, and the reporting time is shown as two minutes, whereas Snowflake is taking just two seconds for the same reporting services.""All the people who are working with Snowflake are extremely happy with it because it is designed from a data-warehousing point of view, not the other way around. You have a database and then you tweak it and then it becomes a data warehouse.""The solution speeds up the process of onboarding.""It is a very easy-to-use solution. It is user-friendly, and its setup time is very less.""The initial setup is very simple.""Snowflake is an enormously useful platform. The Snowpipe feature is valuable because it allows us to load terabytes and petabytes of data into the data mart at a very low cost.""The Time Travel feature is helpful for accessing historical data and the ability to clone external tables is useful.""The way it is built and designed is valuable. The way the shared model is built and the way it exploits the power of the cloud is very good. Certain features related to administration and management, akin to Oracle Flashback and all that, are very important for modern-day administration and management. It is also good in terms of managing and improving performance, indexing, and partitioning. It is sort of completely automated. Everything is essentially under the hood, and the engine takes care of it all. As a data warehouse on the cloud, Snowflake stands strong on its ground even though each of the cloud providers has its own data warehouse, such as Redshift for AWS or Synapse for Azure."

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Cons
"The refreshment rate of data reaching Redshift from other sources should be faster.""In our experiments, the handling of unstructured data was not very smooth.""Migrating data from other data sources can be challenging when you are working with multibyte character sets.""It lacks a few features which can be very useful, such as stored procedures""The initial deployment was complex.""Amazon Redshift is a little more expensive than other products.""The solution is unable to work fast.""The OLAP slide and dice features need to be improved."

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"The solution could improve the user interface and add functionality to the system.""If we can have a feature where the results can be moved to different tabs, so that I can compare the results with earlier queries before applying the changes, it would be great.""I have heard people having difficulty with the machine learning model, so there may be room for improvement.""Room for improvement would be writebacks. It doesn't support extensively writing back to the database, and it doesn't support web applications effectively. Ultimately, it's a database call, so if we are building web applications using Snowflake, it isn't that effective because there is some turnaround time from the database.""They need to improve its ETL functionality so that Snowflake becomes an ETL product. Snowpipe can do some pipelines and data ingestion, but as compare to Talend, these functionalities are limited. The ETL feature is not good enough. Therefore, Snowflake can only be used as a database. You can't use it as an ETL tool, which is a limitation. We have spoken to the vendor, and they said they are working on it, but I'm not sure when they will bring it to production.""The complexity of the initial setup of Snowflake depends on the use case. However, Snowflake itself, we don't set it up. The difficulty comes from the ingestion patterns, depending on what data I'm putting in, what kind of enrichment, and what additional value we have to add. However, it does tend to get complex because we have a lot of semi-structured data which we need to handle in Snowflake. There have been some challenges.""From the documentation, the black box is not very descriptive. Snowflake does not reveal how exactly the data is processed or sourced.""The design of the product is easily misunderstood."

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Pricing and Cost Advice
  • "Redshift is very cost effective for a cloud based solution if you need to scale it a lot. For smaller data sizes, I would think about using other products."
  • "If you want a fixed price, an to not worry about every query, but you need to manage your nodes personally, use Redshift."
  • "BI is sold to our customer base as a part of the initial sales bundle. A customer may elect to opt for a white labeled site for an up-charge."
  • "One of my customers went with Google Big Query over Redshift because it was significantly cheaper for their project."
  • "Per hour pricing is helpful to keep the costs of a pilot down, but long-term retention is expensive."
  • "It's around $200 US dollars. There are some data transfer costs but it's minimal, around $20."
  • "The best part about this solution is the cost."
  • "The part that I like best is that you only pay for what you are using."
  • More Amazon Redshift Pricing and Cost Advice →

  • "Pricing can be confusing for customers."
  • "The whole licensing system is based on credit points. You can also make a license agreement with the company so that you buy credit points and then you use them. What you do not use in one year can be carried over to the next year."
  • "You pay based on the data that you are storing in the data warehouse and there are no maintenance costs."
  • "It is not cheap."
  • "The pricing for Snowflake is competitive."
  • "On average, with the number of queries that we run, we pay approximately $200 USD per month."
  • "Pricing is approximately $US 50 per DB. Terabyte is around $US 50 per month."
  • "The price of Snowflake is very reasonable."
  • More Snowflake Pricing and Cost Advice →

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    Answers from the Community
    Padmanesh NC
    Mineaki Motohashi - PeerSpot reviewerMineaki Motohashi
    Real User

    Although I verified it only in a specific case, I performed performance verification with Redshift, BigQuery, Snowflake.

    Redshift has data redistribution occurred when searching under various conditions and performance was not good, but Snowflake holds data in small units called micro partitions, and also manages data for each column Therefore, operation like data redistribution was minimal and high performance was obtained.

    Snowflake can also start multiple clusters in the same database, but has an architecture in which conflicts do not occur even when accessing the same data between clusters.

    I recommend you to try it.

    Mineaki Motohashi - PeerSpot reviewerMineaki Motohashi
    Real User

    I am glad that you are already using it.

    Questions from the Community
    Top Answer:Amazon Redshift is very fast, has a very good response time, and is very user-friendly. The initial setup is very straightforward. This solution can merge and integrate well with many different… more »
    Top Answer:The tool's most valuable feature is its parallel processing capability. It can handle massive amounts of data, even when pushing hundreds of terabytes, and its scaling capabilities are good.
    Top Answer:The best thing about Snowflake is its flexibility in changing warehouse sizes or computational power.
    Top Answer:The real-time streaming feature is limited with Snowflake and could be improved. Currently, Snowflake doesn't support unstructured data. With Snowflake, you need to be very particular about the type… more »
    Ranking
    4th
    Views
    7,785
    Comparisons
    5,798
    Reviews
    25
    Average Words per Review
    497
    Rating
    7.7
    1st
    Views
    21,234
    Comparisons
    11,994
    Reviews
    36
    Average Words per Review
    464
    Rating
    8.3
    Comparisons
    Also Known As
    Snowflake Computing
    Learn More
    Overview

    What is Amazon Redshift?

    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:

    • Cluster administration: The Amazon Redshift cluster is a group of nodes that contains a leader node and one (or more) compute node(s). The compute nodes needed are dependent on the data size, amount of queries needed, and the query execution functionality desired.
    • Cluster snapshots: Snapshots are backups of a cluster from an exact point in time. Amazon Redshift offers two types of snapshots: manual and automated. Amazon will store these snapshots internally in the Amazon Simple Storage Service (Amazon S3) utilizing an SSL connection. Whenever a Snapshot restore is needed, Amazon Redshift will create a new cluster and will import data from the snapshot as directed. 
    • Cluster access: Amazon Redshift provides several intuitive features to help define connectivity rules, encrypt data and connections, and control the overall access of your cluster.
    • IAM credentials and AWS accounts: The Amazon Redshift cluster is only accessible by the AWS account that created the cluster. This automatically secures the cluster and keeps it safe. Inside the AWS account, users access the AWS Identity and IAM protocol to create additional user accounts and manage permissions, granting specified users the desired access needed to control cluster performance.
    • Encryption: Users have the option to choose to encrypt the clusters for additional added security once the cluster is provisioned. When encryption is enabled, Amazon Redshift will store all the data in user-created tables in a secure encrypted format. To manage Amazon Redshift encryption keys, users will access AWS Key Management Service (AWS KMS).

    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 is a cloud-based data warehousing solution for storing and processing data, generating reports and dashboards, and as a BI reporting source. It is used for optimizing costs and using financial data, as well as for migrating data from on-premises to the cloud. The solution is often used as a centralized data warehouse, combining data from multiple sources.

    Snowflake has helped organizations improve query performance, store and process JSON and XML, consolidate multiple databases into one unified table, power company-wide dashboards, increase productivity, reduce processing time, and have easy maintenance with good technical support.

    Its platform is made up of three components:

    1. Cloud services - Snowflake uses ANSI SQL to empower users to optimize their data and manage their infrastructure, while Snowflake handles the security and encryption of stored data.
    2. Query processing - Snowflake's compute layer is made up of virtual cloud data warehouses that let you analyze data through requests. Each of the warehouses does not compete for computing resources, nor do they affect the performance of each other.
    3. Database storage - Snowflake automatically manages all parts of the data storage process, including file size, compression, organization, structure, metadata, and statistics.

    Snowflake has many valuable vital features. Some of the most useful ones include:

    • Snowflake architecture provides nearly unlimited scalability and high speed because it uses a single elastic performance engine. The solution also supports unlimited concurrent users and workloads, from interactive to batch.
    • Snowflake makes automation easy and enables enterprises to automate data management, security, governance, availability, and data resiliency.
    • With seamless cross-cloud and cross-region connections, Snowflake eliminates ETL and data silos. Anyone who needs access to shared secure data can get a single copy via the data cloud. In addition, Snowflake makes remote collaboration and decision-making fast and easy via a single shared data source.
    • Snowflake’s Data Marketplace offers third-party data, which allows you to connect with Snowflake customers to extend workflows with data services and third-party applications.

    There are many benefits to implementing Snowflake. It helps optimize costs, reduce downtime, improve operational efficiency, and automate data replication for fast recovery, and it is built for high reliability and availability.

      Below are quotes from interviews we conducted with users currently using the Snowflake solution:

      Sreenivasan R., Director of Data Architecture and Engineering at Decision Minds, says, "Data sharing is a good feature. It is a majorly used feature. The elastic computing is another big feature. Separating computing and storage gives you flexibility. It doesn't require much DBA involvement because it doesn't need any performance tuning. We are not doing any performance tuning, and the entire burden of performance and SQL tuning is on Snowflake. Its usability is very good. I don't need to ramp up any user, and its onboarding is easier. You just onboard the user, and you are done with it. There are simple SQL and UI, and people are able to use this solution easily. Ease of use is a big thing in Snowflake."

      A director of business operations at a logistics company mentions, "It requires no maintenance on our part. They handle all that. The speed is phenomenal. The pricing isn't really anything more than what you would be paying for a SQL server license or another tool to execute the same thing. We have zero maintenance on our side to do anything and the speed at which it performs queries and loads the data is amazing. It handles unstructured data extremely well, too. So, if the data is in a JSON array or an XML, it handles that super well."

      A Solution Architect at a wholesaler/distributor comments, "The ability to share the data and the ability to scale up and down easily are the most valuable features. The concept of data sharing and data plumbing made it very easy to provide and share data. The ability to refresh your Dev or QA just by doing a clone is also valuable. It has the dynamic scale up and scale down feature. Development and deployment are much easier as compared to other platforms where you have to go through a lot of stuff. With a tool like DBT, you can do modeling and transformation within a single tool and deploy to Snowflake. It provides continuous deployment and continuous integration abilities. There is a separation of storage and compute, so you only get charged for your usage. You only pay for what you use. When we share the data downstream with business partners, we can specifically create compute for them, and we can charge back the business."

      Sample Customers
      Liberty Mutual Insurance, 4Cite Marketing, BrandVerity, DNA Plc, Sirocco Systems, Gainsight, Blue 449
      Accordant Media, Adobe, Kixeye Inc., Revana, SOASTA, White Ops
      Top Industries
      REVIEWERS
      Computer Software Company32%
      Comms Service Provider14%
      Retailer11%
      Manufacturing Company11%
      VISITORS READING REVIEWS
      Educational Organization50%
      Financial Services Firm9%
      Computer Software Company7%
      Manufacturing Company4%
      REVIEWERS
      Computer Software Company29%
      Financial Services Firm20%
      Healthcare Company6%
      Manufacturing Company6%
      VISITORS READING REVIEWS
      Educational Organization27%
      Financial Services Firm13%
      Computer Software Company10%
      Manufacturing Company6%
      Company Size
      REVIEWERS
      Small Business40%
      Midsize Enterprise24%
      Large Enterprise37%
      VISITORS READING REVIEWS
      Small Business10%
      Midsize Enterprise55%
      Large Enterprise35%
      REVIEWERS
      Small Business24%
      Midsize Enterprise20%
      Large Enterprise55%
      VISITORS READING REVIEWS
      Small Business15%
      Midsize Enterprise34%
      Large Enterprise51%
      Buyer's Guide
      Amazon Redshift vs. Snowflake
      May 2024
      Find out what your peers are saying about Amazon Redshift vs. Snowflake and other solutions. Updated: May 2024.
      770,141 professionals have used our research since 2012.

      Amazon Redshift is ranked 4th in Cloud Data Warehouse with 59 reviews while Snowflake is ranked 1st in Cloud Data Warehouse with 92 reviews. Amazon Redshift is rated 7.8, while Snowflake is rated 8.4. The top reviewer of Amazon Redshift writes "Provides one place where we can store data, and allows us to easily connect to other services with AWS". On the other hand, the top reviewer of Snowflake writes "Good usability, good data sharing and elastic compute features, and requires less DBA involvement". Amazon Redshift is most compared with Teradata, AWS Lake Formation, Vertica, Microsoft Azure Synapse Analytics and Oracle Exadata, whereas Snowflake is most compared with BigQuery, Azure Data Factory, Teradata, Vertica and Apache Hadoop. See our Amazon Redshift vs. Snowflake report.

      See our list of best Cloud Data Warehouse vendors and best Data Warehouse vendors.

      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.