"The integrated workspace in Microsoft Azure Synapse Analytics where everything comes together, such as Power BI and Data Factory, is very good. Additionally, the ability to do dedicated SQL pooling is a benefit."
"The features most valuable are the simplicity, how easy it is to create a dashboard from different information systems."
"The most valuable feature is the level of processing power, and being able to complete tasks in parallel."
"The solution has been working well overall."
"The pricing seems to be quite fair."
"We've had a good experience with technical support in general."
"We have found that it is easy to develop and to do the analytics in the modules of data."
"Fills the gap between big data and classic data warehouses."
"The technical support on offer is excellent."
"It was relatively easy to use, and it was easy for people to convert to it."
"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 solution is very stable."
"It's ultra-fast at handling queries, which is what we find very convenient."
"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."
"From a data warehouse perspective, it's an excellent all-round solution. It's very complete."
"The snapshot feature is good, the rollback feature is good and the interface is user-friendly."
"This solution needs to have query caching so that if the same query is run and the results are available, it will return the data from the cache without having to re-run the query."
"Indicating what areas need improvement in this solution is a difficult question because the organizations that I am working for are really new in this area. However, an even better more simple interface, or perhaps an extension of a connector app store solution, would be helpful."
"More integration is needed to improve the product for the future."
"The initial setup is complex."
"Documentation could be improved."
"It's a complicated product."
"Integration with other products is an area that can be improved."
"Right now, we are really struggling with the performance. it's not as good as we had hoped."
"Every product has room for improvement, although in this case, it needs some broadening of the functionality."
"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."
"There are a lot of features that they need to come up with. A lot of functions are missing in Snowflake, so we have to find a workaround for those. For example, OUTER APPLY is a basic function in SQL Server, but it is not there in Snowflake. So, you have to write complex code for it."
"It's difficult to know how to size everything correctly."
"Its pricing or affordability is one of the big challenges. Pricing was the only thing that we didn't like about Snowflake. In terms of technical features, it is a complete solution."
"Product activation queries can't be changed while executing."
"They should improve the reporting tools."
Azure SQL Data Warehouse is a Fast, flexible, and secure analytics platform for the enterprise. Azure SQL Data Warehouse lets you independently scale compute and storage, while pausing and resuming your data warehouse within minutes through a massively parallel processing architecture designed for the cloud. Seamlessly create your hub for analytics along with native connectivity with data integration and visualization services, all while using your existing SQL and BI skills.
Snowflake provides a data warehouse built for the cloud, delivering a solution capable of solving problems for which legacy, on-premises and cloud data platforms were not designed.
Microsoft Azure Synapse Analytics is ranked 2nd in Cloud Data Warehouse with 34 reviews while Snowflake is ranked 1st in Cloud Data Warehouse with 41 reviews. Microsoft Azure Synapse Analytics is rated 7.8, while Snowflake is rated 8.4. The top reviewer of Microsoft Azure Synapse Analytics writes "Scalable, intuitive, facilitates compliance and keeping your data secure". On the other hand, the top reviewer of Snowflake writes "Good usability, good data sharing and elastic compute features, and requires less DBA involvement". Microsoft Azure Synapse Analytics is most compared with Amazon Redshift, SAP BW4HANA, Apache Hadoop, Oracle Autonomous Data Warehouse and AWS Lake Formation, whereas Snowflake is most compared with Amazon Redshift, Vertica, Teradata, Oracle Exadata and AWS Lake Formation. See our Microsoft Azure Synapse Analytics vs. Snowflake report.
See our list of best Cloud 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.