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."Easy to build out our snowflake design and load data."
"The most valuable feature of Amazon Redshift is its ability to handle really large sets of data."
"I like it because the usage is very similar to Microsoft SQL server. The structure of the query and the temporary tables are very similar."
"It is very easy to dump data into the tool."
"The product offers good support for the data lake."
"Amazon Redshift is a really powerful database system for reporting and data warehousing."
"It's very easy to migrate from other databases to Redshift. There are migration tools dedicated for this purpose, enabling migration from other databases like MS SQL directly to Redshift. The majority of the scripts will be automatically transposed."
"The main benefit is that our portal for end users is running in AWS, so we can easily connect it to other AWS services."
"The solution is easy to use."
"My company wanted to have all our data in one single place and this what we use Snowflake for. Snowflake also allows us to build connectors to different data sources."
"The snapshot feature is good, the rollback feature is good and the interface is user-friendly."
"It is quite easy to manage."
"The product's most important feature is unloading data to S3."
"The solution's customer service is good."
"The overall ecosystem was easy to manage. Given that we weren't a very highly technical group, it was preferable to other things we looked at because it could do all of the cloud tunings. It can tune your data warehouse to an appropriate size for controlled billing, resume and sleep functions, and all such things. It was much more simple than doing native Azure or AWS development. It was stable, and their support was also perfect. It was also very easy to deploy. It was one of those rare times where they did exactly what they said they could do."
"The most valuable feature of Snowflake is it's an all-in-one data warehousing solution."
"It takes a lot of time to ingest and update the data."
"The product could be improved by making it more flexible."
"Planting is the primary key enforcement that should be improved."
"If you require a highly scalable solution, I would not recommend Amazon Redshift."
"The product must provide new indexes that support special data structures or data types like TEXT."
"The solution has four maintenance windows so, when it comes to stability, I think it would be better to decrease their number."
"This solution lacks integration with non-AWS sources."
"Compatibility with other products, for example, Microsoft and Google, is a bit difficult because each one of them wants to be isolated with their solutions."
"The pricing of the solution should be much easier to calculate or find by yourself."
"There are always a few operation updates here and there that can be made."
"Pricing is an issue for many customers."
"There could be better ELT tools that are appropriate for Snowflake. We decided on Matillion and it seemed to be the only one. There need to be better choices, it would be great if Snowflake provided an ELT solution that people could use. Additionally, if there was a pure cloud-based ELT tool it would be useful."
"Snowflake needs transparency over costs and pricing."
"The scheduling system can definitely be better because we had to use external airflow for that. There should be orchestration for the scheduling system. Snowflake currently does not support machine learning, so it is just storage. They also need some alternatives for SQL Query. There should also be support for Spark in different languages such as Python."
"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 solution could improve by allowing non-structured data, such as PDFs, images, or videos. We cannot see the data."
Amazon Redshift is ranked 4th in Cloud Data Warehouse with 61 reviews while Snowflake is ranked 1st in Cloud Data Warehouse with 94 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.
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.
I am glad that you are already using it.