Snowflake Reviews

We needed a data warehouse and we made a decision on what is the right tool for us as a data warehousing tool by comparing products. We looked into Microsoft Azure, Red Shift and Snowflake. In the end, we decided on Snowflake because it looks more up to date, it seemed much better purposed as a data cloud solution.

It was developed from scratch and dedicated to being used on the cloud and that was what we were looking for. It was not just an on-premises system which was then converted to use on the cloud. It was completely developed from scratch and purely focus on the cloud.

Because it was programmed with that dedication, it has some significant advantages.

The thing I find most valuable is that scalability, space storage, and computing power is separate. When you scale up, it is live from one second to the next — constantly available as you scale — so there is no downtime or interruption of services.

It has something like a time machine, as it is from Apple it incorporates that feature in a way similar to their operating system. So whenever you need a version of the data to test with, you can just go back and take a copy of what was backed up yesterday. It makes some things very easy. It backs up your data warehouses, so for example in our case, a colleague deleted a complete database and we just need to do an undrop on the database and the data was there again.

This helps you to have a development environment with current data. You can just clone your production environment and you have a development environment. Everything you do you can test it on real production data without destroying the production data itself.

These are significant advantages.

The company is constantly working to improve the product. Now they have a focus on data sharing, which is really great. We already share data with others who do not have Snowflake. That alone is already great. But if the other counterparts also have Snowflake, then it is extremely easy to share data. You can control access at low levels and even on the cell level. It is very secure.

With the improvements they continue to make, there is nothing now that I would say I miss or features that need to be added. Maybe there could be some more connectors to other systems, but this is what they are constantly developing anyway.

We have been using this product for two years.

The product is very stable. We never had an issue with stability. It is reliable and it is extremely fast. For example, we had a stock procedure that took half an hour to complete on our SQL cluster, and in Snowflake it was running in two minutes. So that is a significant time savings for just one task.

The number of people at our company currently using the solution depends on what we are trying to accomplish. We have four developers in Snowflake and then we also have users who are leaving data with us for our further analysis. That may be around ten other users.

With the growing data set we have and the increase in the size of our business, we will increase the use of Snowflake, but not with respect to the number of users. We are a small company and all the users who need to use it are already using it. We have more data that we need to load and which we want to integrate before we will make more usage of Snowflake.

There is nothing for us to complain about when it comes to technical support. The response time is really great. Whenever we have an issue there is some delay because they are in San Francisco in the United States so there is a time difference. But when we raise an issue, we get answers immediately. We may not get the solution immediately, as that is not always possible. But we get some type of immediate response and days later we have a solution. The tech support is quite responsive.

We use several products together for our framework. We have our data warehouse which is in Snowflake, we use Domo for standard reporting and we use R for data science analysis.

Before we had Snowflake we had a different solution. We switched to Snowflake because we felt the need to modernize our data warehouse architecture. We were also thinking about having other solutions in the cloud to reduce administration costs. With no effort on our part, we could have a stronger system compared to the effort and cost of doing a similar thing on-premises. This was the biggest advantage of Snowflake. We really do not need to have those administrative efforts anymore. Now we don't take care about when we run out of storage or that we need to buy better CPUs because if we need more computing power, we don't worry about it, we just use it and it is there.

The setup for the product was straightforward. For us, it was a little bit of a challenge because when we implemented the data warehouse, we also changed the architectural concept and we implemented a better framework. Because this framework was new to us it complicated our installation. But Snowflake itself, if you want to use and you have a data warehouse already in place with the right framework, then it is straightforward. You just store your data in and that's it. What you use on top is material for orchestrating all the load jobs. But this is other integrations and other choices that are really outside Snowflake itself.

The initial deployment from purchase until it was up and running in production took two months.

We had a consulting company help us for the initial two months of the setup and then afterward we did everything by ourselves. We were quite satisfied working with the consultants and they helped us to implement quickly. We mainly needed them because we implemented this metadata framework. In the beginning, we had this consultancy for analyzing our platform, which to select and which tools should be used. After we completed this initial portion of the project over the two months, we needed them mainly for completing the implementation of the metadata framework.

Snowflake itself is easy to learn. If you know SQL it is really not very hard. Everything is well documented and it is not a problem.

The whole licensing system is based on credit points. That means you commit to using it and you pay for what you use. You can also make a license agreement with the company so that you buy credit points and then you use them. So if you buy credit points that you think will last you for a year, you pay a certain amount of money and then you have these credit points available. What you do not use in one year can be carried over to the next year and it is that easy. The advantage of buying more is that you get a discount when you buy a bigger package with more credits.

There is not really much advice I can give people considering this solution except that they should use it and enjoy it. It really sounds simple but that is it. Of course, you need to be careful with the usage of your credit points. Because there are so many possibilities in configuring the way you build your data warehouse or infrastructure, the data warehouse might seem logical, but it is not the best with respect to using credit points. You need to be careful about this. It probably takes half-a-year experience and then you will know how to do it. If you don't know what you are doing, Snowflake also helps to optimize your usage so that you do don't use too many credits points. After one year, we realized we had spent a huge number of credit points and we talked to Snowfake and then they came to us and we analyzed our systems together and we optimized the usage.

On a scale from one to ten where ten is the best, I would rate Snowflake as at least nine. Why not a ten is only because maybe there is something better on the market which is a ten that I don't know about. For me, it is already a ten.

My current client is migrating from an on-premise data warehouse to the cloud, and they need to consolidate the data lake. I'm using Snowflake to develop the data lake and build data bots for certain functionalities. I've also used Snowflake to aggregate and clean data for my client's analytics.

I like Snowflake's data exchange capabilities. It can exchange data with downstream systems and other vendor partners as well.

There are some challenges with loading unstructured data and integrating some message queues or brokers. In one project, we had a problem connecting to one of the message queues and we had to take a different route altogether on Microsoft Azure. 

I started using this product about two years ago at my last company. Now, I'm a registered Snowflake partner, so working with Snowflake for one of my clients.

My clients haven't faced many stability challenges. It is a relatively new product that's still evolving. They're using it mainly for POCs, so they haven't faced any problems. 

The most valuable aspect of Snowflake is its scalability. The volume of data, which I have seen across almost in terms of five terabytes and other terabytes of data, I didn't see issues.

Snowflake technical support is good. At my previous company, they had a customer success officer for critical accounts, and they were accessible. When I registered as a partner, I also got a quick response from support. I would rate their support eight out of 10.

Positive

Setting up Snowflake isn't complex. I've not seen any challenges.

They have different pricing structures based on data usage. I think they have three or four tiers you can scale through. Most of the clients see an advantage from this type of licensing. That's the reason they go for it. It's priced competitively with other solutions.

I rate Snowflake eight out of 10. 

I have been working on Redshift, Snowflake, and AWS RDS Oracle. In the particular case of RDS Oracle, they were migrating from on-prem Solaris equipment to cloud-based RDS.

I would suggest Snowflake for anyone with the need for a reporting/business analytics view of their data that wants only wishes to maintain technical FTE's around processing the data into or out of a data repository but, doesn't want to go to extent of technical management of "AWS clusters" for the data repository.

It was relatively easy to use, and it was easy for people to convert to it. Moved 168 tables and appropriate indices to Snowflake with minimum modification to Current Oracle DDL. The largest degree of change was setting up the corresponding access Hierarchy to duplicate what was in Oracle ( customer had separate permission structures for application vs Admin/support vs direct reporting access to the data).

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 other thing that people found difficult to deal with was that they had several Oracle DBAs who were very experienced DBAs, but they were used to on-prem. They were used to having the ability to turn any dial and flip any switch. Moving to Snowflake did cause some issues there because they had to completely readdress the fact that they couldn't touch the engine, and they had to spend more time analyzing performance.

I probably used it about six months ago. I haven't been working with a client who is currently on this platform.

I haven't had to call on them for a problem at that level.

It was a cakewalk. The biggest thing that's hard to do with it is that you have to do an analysis of performance over time to determine the scale because they separate compute and storage.

Scaling the query to a proper size compute is the larger aspect of the problem for most people. That's because you're looking at something completely different. The problem is that you're now trying to figure out what is the largest compute you need to keep performance where you want it without going too large. If you were in an on-prem scenario, you would tweak and twaddle all the dials. You might rewrite the query, but at the end of the day, you're still working inside the same physical acquisition or same physical resources, whereas in Snowflake, you're literally saying that you've got a 10 million row table as part of your query, but what is the necessary compute facility that you need to run queries that are running against that table.

It is hard to say because we're usually engaged in the transition as opposed to the long term. Their storage costs are easily within pennies of what AWS S3 would normally cost. 

Most of the clients I've been working with are in the financial sector, and they're relatively small. I would put them in an SMB connection. The first thing we have to bring up for people is that they're going to build this. They shouldn't store their data in S3. They should pipeline directly into Snowflake and use it on their storage. So, the cost is a big issue because these are small to medium size companies, and that is the biggest thing we had to price point for them.

The biggest conversion problem we've seen so far is when someone had a large number of stored procedures that were SQL-based, as opposed to external stored procedures written in C or whatever the DBMS would support. Converting those stored procedures either to a SQL script or to a stored procedure or function that's based on JavaScript is the biggest challenge that most people we've dealt with are having. That's because they have to relearn the language they're writing their logic in.

I would easily rate it an eight out of 10.

The primary use case for Snowflake is in our data warehouse project. We have a private DW and whoever has the credentials can access it. I am a data integration developer and we are using ETL tools to extract the data from different source systems and then load it in the data warehouse.

Snowflake is the latest technology. It has great flexibility whenever we are loading data and performs ELT (extract, load, transform) techniques instead of ETL.

This solution automatically performs micro-partitioning when the data is loaded. This creates a dynamic partition and based on the cluster, the performance is fast and really impressive.

Snowflake is using MPP, massively parallel processing techniques, which is a great feature. It saves developers time and allows us to focus more on client requirements.

It is difficult in some cases to perform ETL and this is something that should be included. As it is now, I use Informatica PowerCenter to load data from on-premises to the Snowflake cloud-based data warehouse. If this could be done by Snowflake directly, without an external integration tool, then it would become a full package. It would be awesome.

They do have a native connector to connect with integration tools for loading data, but it would be much better to have the functionality built-in. We would like to be able to just write an SQL query and do our work.

I have been working with Snowflake for six months.

We have not had any major issues with stability.

Snowflake does not require manual scaling because it does it for you. Developers just need to load the data and process the query. That's it. The developer's job is not to spend time improving performance, as it was with an on-premises solution. We had to do the partitioning, collect the stats, and everything else. In the case of this cloud-based solution, it doesn't require as much work. Instead, we can focus on the queries.

We are planning to increase our usage of Snowflake.

I have been in contact with technical support many times and it was awesome. I got great support. Whenever I needed anything they were ready to help me out, which was nice.

I have experience with Informatica PowerCenter and Oracle. PowerCenter uses ETL techniques instead of ELT. Oracle does not automatically perform micro-partitioning. Instead, you have to partition manually and it is a static partition.

Prior to Snowflake, I was using an on-premises data warehouse. Snowflake is the first experience I have had with a cloud-based data warehouse. It is an awesome tool.

There is no need to install this software, which is the best part. It doesn't require any maintenance, and although DBA support is required, it is much less compared to an on-premises solution. This type of cloud-based solution has no requirement for software, hardware, or maintenance because everything is managed by Snowflake's system.

The only assistance I had during the setup was from the integration tool, Informatica PowerCenter. This was used to export our on-premises data from Oracle and import it to Snowflake on the cloud.

You pay based on the data that you are storing in the data warehouse and there are no maintenance costs.

This is an awesome tool, but there is room for improvement in terms of integration. Also, in terms of management, nothing is perfect and everything can be improved.

I would rate this solution an eight out of ten.

Snowflake is a real-time and cloud-based complete ETL tool. You can receive the beta from various sources from Amazon. You can run your reports and do analysis in  Snowflake. Informatica and Tableau should have done this. Snowflake is a modern version of Informatica which is 100 percent in the cloud.

Snowflake has a variety of other ETL provisions that they provide. You can use your own ETL pipeline. Additionally, they provide adapters, and they are always evolving, it is a well-developed solution.

Snowflake has to build more capabilities because they have only built very few adapters, but they're growing and they're building. They should provide provisions to collect ETL pipeline capabilities, reduce developer work, and make more rapid application development, rather than some customizations. There are very few options, but they are building. I hope they will build ETL rapid application development provisions with more variety.

I have been using Snowflake for approximately eight months.

Snowflake is highly stable.

Snowflake is a cloud solution that provides great scalability. However, I am not sure if it is cost-effective.

We have approximately 30 engineers using this solution. We have plans to scale our usage in the future. This is going to be a futuristic solution.

We have not had any problems with the technical support.

The initial setup of Snowflake is straightforward. To set up the ETL pipeline, pull the data, and then generate the reports takes approximately two hours, end to end.

I did the implementation in-house. We have a three-member team that does the maintenance of Snowflake. However, the amount of people needed depends on the size of the pipeline.

Snowflake licensing is more flexible and it is cheaper than other solutions. I can use it for only 10 days for MVP, or three years, and for flexible models. I can scale up, or down, and the pricing is based on the volume and duration. There are many licensing permutation combinations available.

I rate Snowflake a nine out of ten.

I use this solution for actively building out the cloud data warehouse and data platform for enterprise level customers as well as startups. Generally, our clients are looking for a data warehouse on the cloud to enable them to scale infinitely at a lower cost. I've worked for a finance analytical team building their data lake, the data platform on top of Snowflake, as well as for a telehealth team. It's basically about getting data from multiple sources and building out an entire data platform with data governance. We are customers of Snowflake. 

One small company I worked with had a MySQL RTS based instance and were using AWS RDS with MySQL on top of that. As a result they were unable to scale their database because there were around half a million queries being run per second as well as data querying and data updating. The migration to Snowflake helped the company because there are no limitations in the cloud and no longer restrictions on the queries. Performance for end users improved whether they were internal or external clients. They used to sell the data through APIs so this migration helped to grow their business overall as well as the ML team efficiency and the productivity of users who previously used the data platform. 

The most valuable feature of Snowflake is the query performance. Snowflake is the most efficient way for real-time dashboards or analytical business intelligence reports to be sent to the customer. There are a couple of areas where they have recently improved. One of the key features they introduced is an internal, table-based merch as well as storing of the unstructured data. You can now build a table out of unstructured data, metadata. This hasn't yet been officially announced.

Although the UI has improved lately, they still need to work on their UiPath, the workspace area.

I've been using this solution for two years. 

It's an infinitely scalable system, but if you use terabytes or petabytes of data, then you need to tune the levels. Each day, we get four to five gigs and overall, our data warehouse has 100 gigs plus, it's huge data. 

Our clients previously used the RTS based MySQL and migrated to Snowflake from there. The primary reasons they moved was because of scalability and performance. Other than that, Snowflake reduces costs quite significantly. I also have experience with BigQuery which is particularly used for Google Cloud although these days they have a multicloud enrollment. Snowflake is vendor independent so you don't have to stick everything in Google Cloud. In terms of performance, Snowflake is faster than BigQuery. 

The advantage of Snowflake is that it's easy to deploy and they take care of the setup. Basically, it's a cloud warehouse and doesn't need to be registered on any website. It's easy. It just requires dedicating space and registering. It shouldn't take more than a couple of days. 

Snowflake is reasonably priced, close to half the cost of some other solutions. 

In terms of performance the solution is good when compared to the analytical workloads and good in comparison to Redshift or BigQuery. The performance is on a slightly higher level, but when it comes to real-time performance, NoSQL is better than Snowflake, but that's in rare cases and depends on the particular requirement. Overall, for the analytical use case, Snowflake is a good solution and in terms of availability, it's a cloud data warehouse, so  they do replication and the like. 

It's important to understand your business needs, because these tools need to be properly modeled and they have their own advantages. If you're new to Snowflake, it's worth starting slowly for one month and move gradually, because if it's a complex system and you move everything to Snowflake without good architecture, then you can get stuck with the original problem. It's worth taking the time to make it efficient and then design modeling; there are SnowPro certifications as well. 

I rate this solution an eight out of 10. 

We're running a POC to test scalability, performance, on-demand resource management, workload management, et cetera. The security aspect will also be important for us.

The product offers a lot of great features.

Architectural-wise, it's got great architecture. That's what we are looking for. It's kind of decoupled from storage and has virtual warehouses. We like that we can travel and keep things virtual. For us, the virtual warehousing is likely the most valuable aspect. You can spin up as many virtual warehouses that you want. That's quite useful as a feature.

I haven't found that the solution is lacking any features. It's quite complete.

I would like to see a client version of the GUI. Right now, it is a web GUI, which has stored the SQL, the worksheets. We are asking for that from Snowflake. We'd like to understand how can we save these worksheets on our local desktop. That is not there at this point.

I've been using the solution for about the past year as part of a POC.

In the past, we've used an SQL server. On the cloud side of it, we do have some experience. Snowflake, however, is the new data warehouse solution that we are looking into it.

If necessary, we may have to get into Synapse, and do a kind of a pilot project with that as well. We may evaluate them both to see which is better. We are not there yet. We are just working on Snowflake.

We are a direct customer and end-user.

We've been using the solution during a POC for the last year or so. It's a pilot project to test its feasibility for our company. We're just starting to get performance stats and stuff like that.

I'm not sure which version of the solution we are currently using. I don't recall the exact version number. Usually, people are running the latest version. Whatever the latest available option is is likely the number we are on.

I'd rate the solution at an eight out of ten. We're still in the POC phase, however, based on what we have seen, we are quite satisfied.

We have different data models established on Snowflake so our primary use case is to store data from different sources, such as Azure Data Factory, or Databricks. We use it to create the data coming from different sources, and then we store the data. In addition, we also have a reporting structure that we use. We are partners with Snowflake and I'm a lead data engineer. 

The most valuable feature of Snowflake is the Mbps they have established because it's quite a bit faster than any other data warehouse. The solution has different warehouse sizes in place, and we're able to switch through and choose what we want. It can be done in one or two seconds. In other data warehouses, it may take around one minute to set up a different configuration. In Snowflake, it can be done in, two or three seconds. And apart from that, the most important thing given that I have to analyze all this, is that it's very good with the time travel, it's a great feature. 

One thing which I believe Snowflake needs to improve is related to product activation queries. It's not possible to change the warehouse size while executing. It means, for example, that the query is sometimes activated on a larger surface even if I've switched to extra small, the grading in the background is running on extra large. The result is that I have to pay for extra large until the execution is complete. The cost implications can be quite large. There should be a system where they provide a prompt so you can know the current warehouse size.

It would also be helpful if they would simplify the process of using Snowpipe. The way it works now is quite complicated. There are certain steps that you have to follow which is fine but if they could simplify that process, it would be helpful. 

I've been using this solution for a year. 

I've only had one issue related to stability in the past year. 

It's a scalable solution, we have unlimited storage in Snowflake.

The technical support is quite good. We've used them when we've had to implement something and we need some guidance.

The amount of data which we are currently handling, cannot be handled yet in any other data warehouse, but we do have experience with Azure Data Warehouse. We don't have to manage anything with Snowflake, but with Azure we do. Snowflake is much faster, and I believe it is cheaper as well. They have developed it on top of their own system and integrated the concept.

The initial setup is straightforward. It doesn't require any hardware. It's just about getting the account access and having a browser. Everything is done by Snowflake. There is a set process that you need to follow. It is a really fast process, and it helps that the entire migration can be done within minutes. The solution is used on a daily basis by hundreds of people. 

It's pay-as-you-go with Snowflake. The cost is around $US 50 per DB. Terabyte is around $US 50 per month. There are no additional costs. They are divided into two steps to assist the computer power as well as the storage power. Computation cost is relative to the warehouse size. However long our warehouse is up and running, we are charged.

I would definitely recommend this solution. 

I would rate this solution a nine out of 10.