Snowflake Reviews

Our current plan and use case for this solution is to migrate the data from on-premises to the cloud. We are currently using on-prem monitor data and providing it on the cloud. We are using Snowflake so that once the data is in there, we are trying to create shares over it so that external systems can't consume it.

The features I found most valuable with this solution are sharing options and built-in time zone conversion.

I see room for improvement when it comes to credit performance. The other thing I'd like to be improved is the warehouse facility. 

In the next release, I'd like to see easier connectivity to the on-premises tools.

I have been using this solution for about three years.

My impression from using this product for the last three years is that it is quite stable.

My impression is that this is a scalable solution. There are around 200 users of this solution at our company, including three administrators.

The initial setup process was not that complex and it took around six months.

The implementation was done by an in-house team.

My impression is that the pricing of this solution is reasonable.

I would rate this solution a seven, on a scale from one to 10, with one being the worst and 10 being the best.

We're using it more for data warehousing and distribution.

Snowflake is a SaaS platform, so I'm using whatever is the latest version.

It's definitely for compute. The best use case of Snowflake is massive compute. With the parallel reads that we can do from Snowflake, we can combine data from disparate sources, consolidate it, and provide it to end clients through custom stored procedures.

It's user-friendly. It's SQL-driven. The fact that business can also go to this application and query because they know SQL is the biggest factor. So, we can provide all the data, and the analysts, data scientists, and product strategists can go and analyze the data themselves.

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.

I'd like them to look into the limitations of REST API. Snowflake came up with this native API concept, but it has got a lot of limitations. I'd like to see it provide better service-based APIs so that it can provide data as a service.

I've used Snowflake for over three years.

Its stability is fine, but of late, I get loads of messages saying there's some sort of outage or some sort of issue in the application. I keep getting these notifications from Snowflake, which gives a false impression that something wrong is happening, and it might be underlying in the backend. It doesn't seem that stable.

Its scalability is high. I'd rate it an eight out of ten in terms of scalability.

At this time, we have no plans to increase its usage.

Their support is good.

Prior to Snowflake, it was a completely Greenfield requirement.

It was very straightforward.

It required just two people. One from the Snowflake perspective, and one from my team members' perspective to get the configuration running. That's it.

We haven't yet seen a return on investment because some of the applications are yet to be fruitful and make revenue. We have used Snowflake for the past three years at this point, but we have not yet made great revenue.

It's expensive.

Snowflake is very useful as a data lake and as a data warehouse. Also, it has a lot of features with respect to data science. We are not there yet, but if there are any specific use cases around compute, data distribution, and data sharing, then Snowflake is a tool to be considered.

I'd rate Snowflake a seven out of ten.

We implement this solution for our customers. It is a cloud data warehouse. It is SaaS, and it can be run on Azure, AWS, or something else. We are using its latest version.

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.

In future releases, it can also support full unstructured data.

We have been using this solution for a year.

It is stable.

It has very good scalability. Your data can grow in the platform. We have at least 50 users of this solution in an organization.

Their vendor is wonderful. I only have good words for them.

It is not too complex. Its implementation is easy even for those people who don't know Snowflake and are coming from other environments, such as Oracle or SQL Server.

It can be implemented very quickly. Our customers in Israel implemented it very quickly. It was much faster to implement than other platforms.

It is on a monthly basis. It is based on your usage. There are no additional costs from the point of the licensing fee.

We do give some kind of evaluation to the customers about how much it is going to be. You can decide in Snowflake the virtual machine that you are using for customers. There are several kinds of virtual machines that you can use. It is similar to the clothing sizes: small to extra large. If you need more power in the coming month, you can decide in advance and take a more powerful machine. You can just select it from the platform. You can also decide which machine you want to take for extracting data.

I would advise others to check themselves how fast its implementation can be and how responsive it is. I would also recommend evaluating it before choosing other solutions, such as Microsoft Synapse or Amazon Redshift. You can test it yourself by using a test case. You can try to load the data on each platform, which can take a few weeks, but you will get to know the advantages of this solution. It is very different from other solutions.

I would rate Snowflake a nine out of ten.

I mostly build or design data warehouse analytics solutions using Snowflake.

The productivity across the teams that use Snowflake I believe is higher than it would be without the solution.

Snowflake has three great features: Snowpiping is proving to be very valuable, Time Travel is excellent, and Snowpipes are another great functionality the solution has made available.

Snowflake needs transparency over costs and pricing.

I have been using the solution for three years.

I give the stability an eight out of ten. We rarely have issues.

I give the scalability an eight out of ten.

I have used many on-premise solutions in the past and also Azure Synapse Analytics.

The initial setup is straightforward. I give the setup an eight out of ten.

A basic implementation is quick and we only have to set up Snowflake and the cloud service. We need one engineer and one designer for the implementation.

Depending on the complexity of the implementation, we may need to use a third party or help from the vendor.

Part of the problem with the pricing is that it is very difficult for businesses to get an idea of how expensive it might be until they actually start using Snowflake. Until they start pushing large amounts of data through, we will not really know. I believe that it is very difficult for businesses to make a commitment.

I give the solution an eight out of ten.

My advice is to start Snowflake and not spend too much time thinking about how we could use the solution or what it could be used for. The key is just getting started.

We are also using Apigee we have various consumption patterns, data enrichment, and few shedding of the data, and everything goes into Snowflake. If it is multiple consumers, it goes into AMQ, Kafka, or multiple streams to consume. There are specific APIs that we offer after we send the data into the S3 bucket. We have Apigee APIs for consumption, and there are three to four different patterns. For example, we enrich the data, flatten it, and structure everything before the customers going to go into Snowflake. 

There are going to be specific clients who need specific data from the overall data lake, those are going to be exposed as APIs. We have multiple customers needing the same data and for this, we move them into the streaming Kafka.

Apigee does not communicate directly with Snowflake. We have data registration, and everything is coming into something that is called the trusted bucket. The  Apigee interface API is written off the S3 bucket. The S3 bucket data is moved into the Delta Lake, and where the data are stored from the Delta Lake, it sends it to Snowflake. We have Apigee going to Delta Lake and S3 bucket, but  Apigee does not go to Snowflake, these are two areas where it goes to. 

We have Kafka consuming directly off Delta Lake, and it sends data to Kafka through the AMQ. We have its setup, and we have interfaces that come directly to Snowflake to pull the data. It is then flattened and enriched, and it is used for many purposes, such as reporting.

Snowflake's most valuable features are data enrichment and flattening.

I have used Snowflake within the last 12 months.

The complexity of the initial setup of Snowflake depends on the use case. However, Snowflake itself, we don't set it up. The difficulty comes from the ingestion patterns, depending on what data I'm putting in, what kind of enrichment, and what additional value we have to add. However, it does tend to get complex because we have a lot of semi-structured data which we need to handle in Snowflake. There have been some challenges.

Snowflake has multiple implementations. For example, it can be implemented on Amazon AWS and on-premise. The data between these two cannot work together because they have different time zones. That's where the integration can be difficult because it is similar to them being on separate islands, they are completely separate. At some point, everything is going to go into the Amazon AWS Snowflake, but right now there are two islands that are completely different. We have to pull the data out and send it out again separately through a different pipeline.

In the future, this type of implementation should be easier. The integration could be better. 

I rate Snowflake an eight out of ten.

We use Snowflake for our data warehouse. Amazing product. Redshift cannot compete with a true elastic data warehouse where you can scale computing by just issuing a SQL query (increase computer power) and resizing it down or putting computing unit to sleep. 

Snowflake has many more features:

When combined with Alooma, it's the best data integration system. No need for Talend and all these cumbersome tools.

We were able to implement the entire data eco-system in less than five months, from data integration, data warehousing, ELT, producing fact and dimensional tables, and finally reports.

• Computing unit is accessible via SQL: being able to turn them on or off as needed.
• Snowpipe (ingesting data)
• Looking back in time (being able to look at data in the past within a query)
• No data warehouse management
• Support for JSON.

The list is pretty long.

• Snowpipe auto-ingest should be automatic.
• A better client UI or command line tool: I think SnowSQL is a little awkward.

Excellent.

Excellent.

Used it at a previous company.

Yes. No hardware or server config is needed. Just create a user account.

In-house.

Very good.

Snowflake computing is very affordable. Less expensive than Redshift.

Yes. I looked at Redshift and Vertica.

The primary use case is for building a database and data link.

I like the ability to work with a managed service on the cloud and that is easy to start with.

From the documentation, the black box is not very descriptive. Snowflake does not reveal how exactly the data is processed or sourced.

I have been using Snowflake for three years now.

The stability is reliable and a standard product.

The scalability is very good and we have around two hundred data sets currently operating.

Technical support is good. It is readily available and they are very responsive.

Positive

The initial setup was straightforward.

You can do the implementation in-house since it is a managed service and only takes a few hours.

The pricing is economical as compared to traditional solutions like Oracle and competitive pricing.

I would rate Snowflake a nine out of ten.

Snowflake is good for analytical purposes when you have a lot of historical or sales data that you need to release and use for different types of analysis, such as tracking sales and measuring the performance of your sales team and product.

The product's most important feature is unloading data to S3. It provides a single syntax query to analyze data directly from a database to an S3 bucket.

The product's performance could be improved.

I have been using Snowflake for a couple of months. We are using the latest version.

The product is stable.

We did not need to scale our Snowflake environment beyond what we needed. We have a fixed amount of traffic from a fixed number of clients. We know the load we need to handle, and based on that, our subscription is made.

Snowflake is cost-effective. The pricing is better than Firebolt. Firebolt is better when there is idle time. If we run Snowflake all the time, the cost will be higher.

We are working on two solutions for Snowflake, one for the cloud and one for on-premises. It has good documentation. If someone goes through it, they will quickly understand how it works. However, Firebolt's documentation is more comprehensive. If I need faster results, I'll prefer the Firebolt; if I need performance, I'll use Snowflake.

Overall, I rate it a nine out of ten.