Snowflake Reviews

The primary use case is data platforms, specifically data warehousing. It involves restoring and moving data within the platform to prepare it for analysis, routing activities, or serving as the backbone for applications. 

Snowflake also advertises different workstreams, but my customers mostly use it as their core platform to ingest data and serve the onward goals of the wider company.

The most valuable feature of Snowflake is consumption-based costs, which means that you only pay for the storage and compute you use. There's a complete separation of storage and computing, so you don't need to add another server to increase storage or computing. From a costing perspective, it's well-positioned. 

Snowflake's time travel is also incredibly useful, and they have a function called "UNDROP," where you can undo a table drop. Data sharing and replication for Snowflake are strong, and they have a data marketplace with public and private data sets available for sharing. Companies can put their data on the marketplace, and anyone can use it by starting the payment model. The data is provided live straight to you, and it appears as if it were just another database in your own environment.

The main thing I'm excited to see at some point with Snowflake, hopefully - I've not seen anything coming out of it yet - is Git integration into the worksheets and the UI. Sometimes it can be tricky to manage multiple environments if you're purely using Snowflake as your scripting and pipeline environment. This is handleable, so if you use third-party tools like DBT, Matillion, etc., those can help. But if you're looking purely within Snowflake itself, it'd be great to have some form of Git support.

For the future releases, I would love it if they one day decided to implement their own GUI-based transformation tool environment. I know that many competitors like Azure have to Sign Up, and Azure Data Factory can sit in. However, Azure is a very different beast that serves all sorts of different processes, and an argument could be made for whether it's the best to each of those or not. Specifically within Snowflake, I would love it if they could get some form of orchestration built-in for transformation that doesn't have to be controlled directly through code all the time.

I have been using Snowflake for five years. 

It is an incredibly stable solution. It will only go down if your cloud provider itself goes down. So, let's say your Snowflake is hosted in Azure London. If the Azure London data center goes down, I would only see Snowflake going down. If that does happen, Snowflake does have plenty of options for failback replication and rollover backups. 

So we have quite a few customers that, for example, need their data restored in AWS London, and they've got a backup or a replication stored in Azure London. If AWS London goes down, then Azure London one will kick in and become the primary account, and all of the URLs, etcetera, remain the same because they've set up failover URLs and connections for it. At least for the end customer, there's no change. It's only for the architecture and developers behind the scene who then have to double-check things and do all the normal due diligence. But it runs very smoothly

It is a highly scalable solution. There is no limit on storage or computing. They have everything on consumption-based pricing, but you can have what's known as a multi-cluster warehouse. So, warehouses are what you use for the compute.

The multi-cluster warehouses will sit there originally as a single cluster. But then, if there are enough concurrent queries taking place in that warehouse, it can, as it needs, just spin up another one from another one and another one to meet those current needs. And as soon as they can dive down again, it can switch those clusters off again one by one. And you can create as many clusters, warehouses, as many as you need. There is no scaling issue at all. I've seen it most, like, 10,000 queries a second, and it's run very, very smoothly.

The customer service and support team is very useful and strong. They've got support built directly into the Snowflake UI. So wherever you are on the platform, and you see an issue, you can click into the support area and submit your ticket, including direct things like the query ID that you're using or multiple query IDs and all that stuff. 

I find Snowflake to be very responsive, and if you submit a top-level ticket, you can get a response very quickly. The lowest tier of tickets might take 48 hours sometimes, but overall, they are very helpful.

I personally don't see any of the competing cloud platforms coming close right now to what Snowflake offers. An argument could be made with GCP and Datadog are getting closer. Also, a new AWS Redshift is on the horizon, like a whole new AWS Redshift 2.0. But right now, I've not seen anything that comes close. Snowflake, to my understanding, is the only platform that fully separates your storage and computing, essentially. And it's the only platform I've seen with things like time travel. It's got a whole bunch of great features that I don't know if other tools also have, but it supports semi-structured data. It supports automated tasks, alerts, and reporting. And the data sharing is a massive one. GCP now also has its own data-sharing potential, where you can share data with other GCP accounts. I've not used it myself, but to my knowledge, whilst they have the sharing, they don't have anything that even comes close to the Snowflake data marketplace that allows customers to sell or share their data outside the wider world. And it doesn't have anything that comes close to the kind of private equipment where customers might share their own data internally or to their own. And I think there was one more thing. 

Snowflake also have some really good support for Python, Scalar, and Java through what they call Snowpark, which was launched last year. But more recently, this year, it was announced they're really pushing forward with their StreamLINK integration. It will allow customers to host applications on Snowflake and share those applications with other users in a very similar kind of marketplace environment they use for data sharing. I don't think there's anything that any of the other competitors have right now.

The deployment model is delivered as a service. So the most deployment you have to do yourself is by deciding which cloud provider and region you want it to be hosted in. But Snowflake will actually host it themselves, so there's no deployment beyond clicking from a dropdown and clicking okay, and it'll magically appear.

Moreover, it's very easy to maintain because it's delivered entirely as a service. Snowflake takes care of all the patches, upgrades, maintenance, security tweaks, etc.

We have many long-term customers who have been using Snowflake for years, and they wouldn't continue to use it if they weren't seeing a strong return on investment.

There are many options for starting a Snowflake deployment, but I recommend working with a partner who can provide best practices and guidance. It could be through Snowflake directly or another service partner. Working with a partner can save you time and prevent mistakes down the road.

Overall, I would rate the solution a ten out of ten. 

I am using Snowflake for migrating data and table backups.

The most valuable feature of Snowflake is its performance. We can access the data quickly. Additionally, it handles structured and non-structured data.

The solution could improve by allowing non-structured data, such as PDFs, images, or videos. We cannot see the data.

I have been using Snowflake for approximately three years.

The stability of Snowflake is good.

Snowflake is a scalable solution.

We have approximately 200 to 300 people using the solution.

The initial setup of Snowflake is easy.

If the use case fits the solution then I would recommend it. For example, if you have large data and want the rational database backed up, this solution would be a good choice.

I rate Snowflake an eight out of ten.

In our organization, data is often spread out over multiple on-premise and cloud-based platforms. Snowflake is an agnostic platform, meaning it can be used regardless of which cloud provider we use and can serve as a single source for all of our data. Our use case then is to ensure that all data is located in one place, utilizing Snowflake as the platform.

With Snowflake, productivity can be increased. Snowflake is faster than on-premise systems and allows for variable compute power based on need. We are currently experimenting with ways to reduce processing time from eight hours to six hours or even less.

The vendor claims that the provision of warming sensors quickly enables warmth to compute nodes, which is their Unique Selling Point. From my experience, this has proven to be true.

For the past year and a half, I have experimented with different proof of concept. I have wanted to use Snowflake, however, I have not been able to do so. Microsoft Azure is superior to Snowflake in terms of its machine learning and artificial intelligence capabilities. Snowflake has its own unique products, but Azure surpasses it in those areas. Snowflake can improve its machine learning and AI capabilities.

I have been using the solution for one and a half years.

I found that Snowflake is highly stable, which is a reflection of the quality of its network.

I have gone to the extremes from a scalability perspective and I give Snowflake's scalability an eight out of ten.

As a premium customer for all these solutions, we have received excellent support overall with no issues encountered thus far. However, it is possible that some users of the free version may be experiencing some issues. I cannot confirm this, but from my experience, there have been no difficulties.

I give the initial setup a seven out of ten for ease and time required.

We are leveraging Snowflake, a cloud-based platform, both independently and in collaboration with the vendor. Our objectives for utilizing Snowflake are distinct from those of the vendor.

Comparing Snowflake to on-prem options such as Oracle or SAP, it seemed more cost-effective. With Vantage, a one-time purchase allows for use as many times and to a large capacity, whereas Snowflake, Azure, and similar services become increasingly expensive as the scale increases. Determining the best point of cost-effectiveness requires further study.

I have tested all the different alternatives to Snowflake as well. It is hard to determine which one is the most suitable, as each has its own advantages and disadvantages.

I give the solution an eight out of ten.

Deciding between Azure Synapse and Snowflake can be difficult, as the best choice depends on one's own use case. Ultimately, it comes down to the available connectors; the product with more connectors is likely the better option. When making a decision, one should consider which other sources they would want to get data from and where they want to send data to. This can help inform their product selection.

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 have day-in and day-out data coming from our various heterogeneous source systems. We read from that and we load data into our target tables, or rather data lake and data warehouse that is built into Snowflake. We can even translate and delete items. Apart from that, we use the tasks and schedule jobs within Snowflake. Once the data is generated into the final data material, we share it with the client for their review.

Time travel is one feature that really helps us out. If there is some data discrepancy or something wrong that happens with the table, at any point, we can get that data back and time travel via fail-safe features. They're really good. 

The feasibility to increase horizontal or vertical scaling is great. It really helps a developer at an enterprise level. If you're considering scaling, it won't take much time. 

We can create cloning via Snowflake. There's a lot of time savings.

The initial setup is simple. 

The solution could use a little bit more UI. Something was in process, however, it's not yet deployed or in the version that I'm using it is not deployed. If we have to use any table or any schema in other DB features, the prompt comes over after a dot. Whenever we are using it it would really help if it looked better. 

If you're running a procedure, it just gives a standard error instead of the exact error captured. If you have to look at it, you need to go into the history to look into which query failed, and we need to figure it out. Instead, if the error, whatever it is, is instead displayed in the history, showing the point of failure, it would be more visible. It would save some time for all the people who are using it.

There are some reporting analytics that we can use. I'm aware of those. However, if there was more reporting, we'd appreciate that. We'd like for it to become a complete one-stop-shop solution. 

We'd like to have some automation around small tasks, especially around scheduling.

I've been working on the solution for two years. 

In the past three years, I have faced instability. In just one or two instances, I would say when we were running the queries, it was not fetching any result and the error was not proper. That said, that happened in just one or two instances, in the lower environments. Otherwise, the product is very stable.

The solution is quite scalable both horizontally and vertically. It's an easy job. We just need to resize it. 

Within our module, there are around eight to ten people who are working on Snowflake. In a full project, if I'm looking at the holistic picture, there are around 30 to 40 people who are using Snowflake.

We've reached out to technical support in the past. In some cases, we were not even able to fetch the data. Or, a couple of times, in the time traveler, the pay phase time was expired and we had to retrieve that data. When that happened, we had to reach out to Snowflake support. In the first instance, they said there was downtime at that server, which we were not informed about. Since it was in the lower environment, it didn't cause an issue. 

Overall, the technical support was really good. They were on it. They helped as much as they could. For a few things they needed some approvals, and they took that. They got back to us in even less time than was required by the SLA.

Positive

We are also working with Teradata.

The main difference is that Snowflake is on a cloud server and Teradata is still a legacy system. Teradata was one of our source systems. We were loading it into Snowflake. With Teradata, when you create primary care, something constrains the way you want to do it so that you do not put an additional effort into your ETL. That changes when it comes to Snowflake. If you're clustering something on a case, obviously you end up paying the extra cost. That is one basic and main difference that we saw.

That said, you can process a huge amount of data in Snowflake. This becomes a challenge in Teradata. When we were reading the data, we had to load that data into Snowflake. When we were trying to read that data, we had to obviously divide it into chunks and then load it into Snowflake. Loading into Snowflake was like a cakewalk. Everything is moving into the cloud now. Legacy systems like Teradata just can't handle the amount of data required. 

I've worked on many of the legacy systems like Oracle, DB2, et cetera. Migration from one environment to a higher environment was a huge thing. The DVS used to take two or three days sometimes depending on how many tables or what the objects are and what they had to create. Now, with the cloning feature that we have, the idle time for the developers, or even the people who are looking at the data, the analysts, and everyone, is reduced to none. We just clone it, we load the data and the data is available without any hindrances.

The solution is straightforward. 

My Azure ID was created by the DBA. As soon as I had to log in, my user role was set up. To log in to the interface hardly took me more than two or three minutes. Compared to our Data Bricks, it was pretty simple. It's hassle-free. 

The maintenance of the DBs and the production services are normally taken care of by the team itself who's handling the production support. However, just in case, if there is something, if a huge mishap happens, which we cannot recover, or we would need Snowflake customer support help, that is when the help is taken. Otherwise, the team handles everything in-house. 

The majority of the cases that we have seen are some queries stuck somewhere and out of the blue. In the case of some source data, it decides to send you a huge amount of data on a particular day. While we have seen some mishaps, a regular case requires our developers to deal with it.

We handled the initial setup in-house. 

The pricing is fair for all the features that Snowflake is providing. The way Snowflake has emerged in the past few years is impressive. It was just a beginner in the space, and it has become a leader in the market. For all the features it provides at a basic cost, it is impressive.

There are no extra costs involved unless you need to scale and grow and need more in terms of size. When we started, a medium-size was sufficient. We'd since needed more. 

The solution allows us to suspend our warehouse as well if we would like. 

That said, even if you're paying a little more cost for the data safety that you have, you will ensure that there is no data loss that will happen. It's very impressive.

My past company was a Snowflake customer. This current company is not, however, it may be on the verge of it. 

I'm using the latest version of the solution. We switched clouds at some point. I was using it on AWS, and now it is on Azure. 

I'd recommend the solution to others. I would rate it 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.

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.

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.