We compared Snowflake and VAST Data based on our users reviews in five parameters. After reading the collected data, you can find our conclusion below:
Comparison Results: Snowflake is praised for its easy setup, valuable features, and good customer service. However, it needs improvement in areas like pricing transparency, data integration, user interface, and documentation. On the other hand, VAST Data is commended for its simple and efficient setup, strong failover capability, and good customer service. It could benefit from enhancing its read/write ratio. The pricing perception and user ROI differ for both products.
"They separate compute and storage. You can scale storage independently of the computer, or you can scale computing independently of storage. If you need to buy more computer parts you can add new virtual warehouses in Snowflake. Similarly, if you need more storage, you take more storage. It's most scalable in the database essentially; typically you don't have this scalability independence on-premises."
"I like the idea that you can assign roles and responsibilities, limiting access to data."
"It is a very easy-to-use solution. It is user-friendly, and its setup time is very less."
"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."
"It is quite easy to manage."
"Working with Parquet files is support out of the box and it makes large dataset processing much easier."
"It helped us to build MVP (minimum viable product) for our idea of building a data warehouse model for small businesses."
"The most efficient way for real-time dashboards or analytical business intelligence reports to be sent to the customer."
"This has been one of the most reliable storage systems that I have ever used."
"The solution is useful for machine learning and scientific applications, including computer simulations."
"The solution could improve by allowing non-structured data, such as PDFs, images, or videos. We cannot see the data."
"These days, they are pushing users towards the GUI or graphical version. However, I am more familiar with the classic version. I'd like to continue to work with it using the older approach."
"There are three things that came to my notice. I am not very sure whether they have already done it. The first one is very specific to the virtual data warehouse. Snowflake might want to offer industry-specific models for the data warehouse. Snowflake is a very strong product with credit. For a typical retail industry, such as the pharma industry, if it can get into the functional space as well, it will be a big shot in their arm. The second thing is related to the migration from other data warehouses to Snowflake. They can make the migration a little bit more seamless and easy. It should be compatible, well-structured, and well-governed. Many enterprises have huge impetus and urgency to move to Snowflake from their existing data warehouse, so, naturally, this is an area that is critical. The third thing is related to the capability of dealing with relational and dimensional structures. It is not that friendly with relational structures. Snowflake is more friendly with the dimensional structure or the data masks, which is characteristic of a Kimball model. It is very difficult to be savvy and friendly with both structures because these structures are different and address different kinds of needs. One is manipulation-heavy, and the other one is read-heavy or analysis-heavy. One is for heavy or frequent changes and amendments, and the other one is for frequent reads. One is flat, and the other one is distributed. There are fundamental differences between these two structures. If I were to consider Snowflake as a silver bullet, it should be equally savvy on both ends, which I don't think is the case. Maybe the product has grown and scaled up from where it was."
"To ensure the proper functioning of Snowflake as an MDS, it relies heavily on other partner tools."
"The cost efficiency and monitoring of this solution could be improved. It's easy to spend a lot on Snowflake and it does offer monitoring tools but they're pretty basic."
"I don't know about GCP, if they have connected for GCP. If they don't, they should allow for it."
"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."
"The aspect of it that was more complicated was stored procedures. It does not support SQL language-based stored procedures. You have to write in JavaScript. If they supported SQL language and stored procedures, it would make migration from on-prem much simpler. In most cases, if an on-prem solution has stored procedures, they're usually written in SQL. They're not written as what most on-prem DBMS would refer to as an external stored procedure, which is what these feel like to most people because they're written in a language outside of SQL."
"The write performance could be improved because it is less than half of the read performance."
"The read/write ratio is an area in the solution with some flaws and needs improvement."
Snowflake is ranked 1st in Data Warehouse with 92 reviews while VAST Data is ranked 8th in NVMe All-Flash Storage Arrays with 2 reviews. Snowflake is rated 8.4, while VAST Data is rated 10.0. The top reviewer of Snowflake writes "Good usability, good data sharing and elastic compute features, and requires less DBA involvement". On the other hand, the top reviewer of VAST Data writes "Stability-wise, a device that has been up and running for years". Snowflake is most compared with BigQuery, Azure Data Factory, Teradata, Vertica and AWS Lake Formation, whereas VAST Data is most compared with Pure Storage FlashBlade, NetApp AFF, Pure Storage FlashArray, Qumulo and Red Hat Ceph Storage.
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