"The solution has many features that are applicable to events such as audits."
"The most important advantage in using AWS Lake Formation is its ability to connect the data lake to the other technologies in AWS. This is what I advise my clients."
"The solution is quite good at handling analytics. It's done a good job at helping us centralize them."
"Its performance is a big advantage. When you run a query, its performance is very good. The inbound and outbound share features are also very useful for sharing a particular database. By using these features, you can allow others to access the Snowflake database and query it, which is another advantage of this solution. It has good security, and we can easily integrate it. We can connect it with multiple source systems."
"The Mbps they have established is quite a bit faster than any other data warehouse."
"I like the fact that we don't need a DBA. It automatically scales stuff."
"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."
"The ETL and data ingestion capabilities are better in this solution as compared to SQL Server. SQL Server doesn't do much data ingestion, but Snowflake can do it quite conveniently."
"Great scalability and near zero maintenance."
"It is a very good platform. It can handle structured and semi-structured data, and it can be used for your data warehouse or data lake. It can load and deal with any data that you have. It can extract data from an on-premises database or a website and make it available in the cloud. It has very fast implementation and integration as compared to other solutions. There is no need for the DBA to manage or do the day-to-day DBA tasks, which is one of the greatest things about it."
"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."
"In our experience what could be improved are not the support, performance or monitoring, but at a managerial level, the very expensive professional services of AWS. This could be an area of improvement for them. It's too expensive to acquire their support."
"For the end-users, it's not as user-friendly as it could be."
"The solution could make improvements around orchestration and doing some automation stuff on AWS front automation. It would be useful if we could use automation to build images and use hardened images which are CIS compliant."
"We would like to see more security including more masking and more encryption at the database level."
"The documentation could improve. They should provide architecture information."
"It would benefit from an administration that allows you to be aware of your credit consumption once you have the service so that you may be sure how many credits you are consuming when you use the platform and to make sure that you are making the most efficient use of these resources. In other words, to improve their interface so that you may monitor the consumption of your credits on Cloud."
"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."
"It's difficult to know how to size everything correctly."
"Their UiPath, the workspace area, needs some work."
"The solution needs more connectors."
Earn 20 points
AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis.
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.
AWS Lake Formation is ranked 9th in Cloud Data Warehouse with 3 reviews while Snowflake is ranked 1st in Cloud Data Warehouse with 42 reviews. AWS Lake Formation is rated 8.0, while Snowflake is rated 8.4. The top reviewer of AWS Lake Formation writes "Good security, excellent online resources and easy to set up". On the other hand, the top reviewer of Snowflake writes "Good usability, good data sharing and elastic compute features, and requires less DBA involvement". AWS Lake Formation is most compared with Microsoft Azure Synapse Analytics, Azure Data Factory, Amazon EMR and Firebolt, whereas Snowflake is most compared with Microsoft Azure Synapse Analytics, Amazon Redshift, Vertica, Teradata and Firebolt. See our AWS Lake Formation vs. Snowflake report.
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