We performed a comparison between AWS Lake Formation 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.
"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."
"The initial setup is very simple."
"It was relatively easy to use, and it was easy for people to convert to 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."
"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."
"It is a very well-distributed system. It has different data engines for different applications. Many applications can use different computational engines at the same time. In terms of data processing, the feeling was similar to working with a relational database but in a scalable way."
"It is a very easy-to-use solution. It is user-friendly, and its setup time is very less."
"The adaptation to development languages is most valuable. Our developers can SQL code or something else. It has been convenient in that regard."
"Time travel is one feature that really helps us out."
"For the end-users, it's not as user-friendly as it could be."
"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."
"Some SQL language functions could be included."
"I would like to see more transparency in data processing, ATLs, and compute areas - which should give more comfort to the end users."
"These aren't as crucial, but there are common errors sometimes where the database is down, or a table is nullified and a new table is added and you are not given access to that. With those errors, you don't have permissions."
"It would be better if they had a data profile tool that tells me where the gaps are in my time series data."
"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."
"The solution could use a little bit more UI."
"The solution needs more connectors."
"Portability is a big hurdle right now for our clients. Porting all of your existing SQL ecosystem, such as stored procedures, to Snowflake is a major pain point. Currently, Snowflake stored procedures use JavaScript, but they should support SQL-based stored procedures. It would be a huge advantage if you can write your stored procedures using SQL. It seems that they are working on this feature, and they are yet to release it. I remember seeing some notes saying that they were going to do that in the future, but the sooner this feature comes out, it would be better for Snowflake because there are a lot of clients with whom I'm interacting, and their main hurdle is to take their existing Oracle or SQL Server stored procedures and move them into Snowflake. For this, you need to learn JavaScript and how it works, which is not easy and becomes a little tricky. If it supports SQL-based procedures, then you can just cut-paste the SQL code, run it, and easily fix small issues."
AWS Lake Formation is ranked 11th in Cloud Data Warehouse with 2 reviews while Snowflake is ranked 1st in Cloud Data Warehouse with 49 reviews. AWS Lake Formation is rated 8.0, while Snowflake is rated 8.4. The top reviewer of AWS Lake Formation writes "Stable and good at handling analytics but needs to better accept integrations". 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, Amazon Redshift, Azure Data Factory, Amazon EMR and Apache Hadoop, whereas Snowflake is most compared with Microsoft Azure Synapse Analytics, Amazon Redshift, Vertica, Teradata and Azure Data Factory. See our AWS Lake Formation vs. Snowflake report.
See our list of best Cloud Data Warehouse vendors.
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