Snowflake Reviews

Mostly, we use it for the data warehousing side of use cases, where you have, like, a huge amount of data, and you are required to do reporting in terms of data science, data warehousing, or ad hoc reporting. The use cases we have used are, for example, data coming from MedTech devices, mostly sensor data, which we need to load in Snowflake and do data analytics. We have been using the tool for a couple of MedTech clients.
 

The most important part of the tool is that computing and storage are totally separated, and it keeps on evolving every two weeks, with the tool having releases. New features are coming up in the tool. With respect to AI, the tool is also progressing well. The scalability and performance are quite good. If you have data, like in CSV or any other format, you can load it very quickly and then do your analysis. Columnar database performance, scalability, and the addition of new features are a few useful features of the tool.

I think people do not want to create pipelines for many customers now. Normally, we have this layer architecture, like layer one, layer two, layer three, or layer four, where we have raw data, integrations, business data, and then semantic data, so we have to create various pipelines. People don't have to create or maintain pipelines since, in the future, if there are any changes in the source data, it should be very easy to configure and create the pipeline rather than the developer doing that for them. Though it may not be possible to make improvements based on the expectations of the people, considering the AI market, code generation can be simplified a little bit by using streams. People want to be able to develop the pipeline without involving many developers by doing some configurations and creating the pipeline. The customer expectation is that they don't want to create tables for each report, but what happens currently is that if you don't create that, then you have to run the query every time. Suppose I have created raw data, and I want to do some aggregation. In that case, if I don't create a materialized view or a table, I have to run those aggregate queries again and again, which will cost me the cost attached to Snowflake usage. From an improvement perspective, Snowflake can evolve in terms of writing costly, expensive queries with less cost and try to see if pipeline development can be made a little easier.

I have been using Snowflake for a year and a half.

There were use cases where there were only 10 to 15 users. There was one requirement where the customer asked for 3,000 concurrent users to try to get a real-time report from the tool, but then our company suggested that Snowflake was not the right choice for them because it is more kind of a data warehouse, and they were looking more into transactional reporting. For Snowflake-based projects where we have worked, it is more concerning a smaller number of users, like around 20 users. However, if a huge number of users are required, Snowflake is not the right choice.

My company has partnered with Snowflake. Normally, we reach out to the account manager or regional manager, and sometimes we get support. Most of the time, we ask for support from the architecture and solutions part of it to review it or for some workarounds. Right now, we have not gone for low-level technical support from Snowflake. Whatever we have worked on, we are able to manage.

I have been working all my life on databases, so I have almost twenty five years of experience in databases starting from SQL, Oracle8i, Oracle 9i to MySQL, SQL Server and Redshift. I have also used Solr and Elasticsearch, which are not databases but all data-related things I have worked on, including PostgreSQL.

The main thing about Snowflake is that it is totally outside the customer's cloud. If I am an AWS customer, even if Snowflake is hosting on AWS, it is on a separate account right now. If somebody has some critical data that cannot be shared outside the cloud, then such customers or people are a little hesitant to use Snowflake. Recently, there were some breaches or password issues, so security concerns like that are there. There is also the costing part attached to the tool. Now, people are looking into tools that are available at a lower cost and offer more user-friendliness. The tool is a good data cloud product, but it is a little bit outside the customer's environment, which makes it difficult to convince the customer to use it.

Speaking about the product's initial setup phase, I would say that the product is used just from the cloud. We have not installed it in any environment. I work with the tool's SaaS version.

The tool does add some value to the company. When it comes to pipeline development work, though customers expect it to be faster, I think if you have simple files, you can load them in a day and analyze the data. Productivity-wise, it is definitely much better compared to Redshift. Redshift Spectrum is catching up with Snowflake, but I have not explored it. To be very frank, I am not very familiar with Azure Data Warehouse, so I am not sure how it is different from Snowflake, but from what I have seen, it has been good in terms of productivity.

The pricing part is based on the computing and storage. The costs are different and then there are services costs as well. I have heard that Snowflake is costlier than Redshift or GCP BigQuery. A small customer may not go for Snowflake.

Speaking of how Snowflake enhances our company's AI-driven projects or analytics, I would say that the tool has features like Document AI and Snowflake Cortex. AI can be used if the tool is for very basic use cases, like anomaly detection or prediction. With simple use cases, you don't have to set up a big infrastructure. You just load data and use the tool's services. I have not used the tool for complex AI projects. I am not an AI person. Rather, I can be described as a data engineer or data architect. In our use cases, we have explored the AI feature of Snowflake more from document processing and doing a simple exploration of the feature. For customers, I have not used Snowflake's AI feature.

Speaking about how Snowflake's scalability feature impacted our data processing and analytics tasks, I would say that the tool has a virtual warehouse, so it really helps. You can scale based on your needs. You can change the warehouse sizing, which will help with the scalability. You can just increase the warehouse size, and it gets your work done.

There are various ways to integrate the tool. I think the tool has connectors also, but the external table is one way to load your data in Snowflake and start analyzing it quickly. Now, the tool also works with Apache Iceberg format, though I have not explored that. With respect to Snowpipe, getting data from CSV to Snowpipe are things we use, and they are all quite easy to use. In terms of native connectors to various data sources, though I have not explored them, I see the tool has support for various connectors. I believe that will be good. For most of the use cases, data is loaded onto S3, and then we use Snowpipe along with external tables and Snowpark ML to process the data.

Snowflake has something called Snowflake Horizon, which has bundled various features of data security, data governance, and compliance together, and they have come up with the package. The tool has very good data security in terms of masking data. You can have different roles and assign policies in terms of who you want to be able to see data of a particular department, so you can assign based on department ID that only certain people can see the data. I found good features in my various other cloud databases, and compared to them, Snowflake data security and data governance are quite capable.

I don't think it is difficult to maintain. As the organization grows, maintaining policies, user roles, and data masking policies might become a little tricky in Snowflake. In AWS, we have a well-architectured framework where you have a defined framework or pattern, and you try to reuse it and modify it as needed. I don't see such kind of information or patterns largely available in Snowflake. I think as an architect, if we have a well-architectured framework for Snowflake, it will be useful. In terms of maintenance, I think the performance and all is okay in the tool. Data governance and policy management are a little bit tedious for the tool.

I recommend the tool to others. People should only be okay with the product's cost.

I rate the tool an eight out of ten.

The solution is very good for building data warehouses. However, it has some limitations if we need it for more use cases.

All the features of the product that are needed for the data warehouse are good. The solution enables the ingestion of data and the usability of preferred languages while creating the data. The performance of the engine is good. The solution speeds up the process of onboarding. We can connect to different sources and get the data very fast. 

The tool does a very good job in reporting and data transformation. We can adapt it well to our needs. When we try to ingest data from many sources, it helps harmonize the data sources. It also helps with duplication and cleaning of the data. It is a pretty difficult and time-consuming task, and Snowflake helps us with it.

I do not like the proprietary format of the solution. Getting data out of the tool to third-party applications is difficult. The data science workloads must be improved. Snowflake has a lot to learn. There are better options in the market for data science.

I have been using the solution for almost three years.

The tool is stable. I rate the stability a nine or ten out of ten.

If we need to scale up, it will impact our costs. I work in a consulting company. We have a department dedicated to Snowflake. We have seven to eight people on our team. Our clients were medium-sized businesses with 1000 employees. They are focused on data analytics solutions. They also have departments for Azure and AWS. I rate the tool’s scalability a seven or eight out of ten.

I rate the ease of setup a seven or eight out of ten. The tool is deployed on the cloud. The time taken for deployment depends on the workload and how we build it.

The solution is expensive. I rate the pricing a nine out of ten.

We are partners. The impact of the solution’s automatic scaling feature on the workload depends on how we build our workload. The vendor must take a look at the market and how technologies evolve. The solution can do more in the area of distributed systems. If our use cases require data scientists, I rate the tool a three or four out of ten. I rate the tool a nine or ten out of ten for SQL data warehouse use cases. Overall, I rate the product a seven out of ten.

I use the tool with visualization tools like Tableau and Power BI. We load the data into these tools and use them to build customer reports. We often need to write scripts to perform transformations before sending the data to the visualization tools.

The tool's performance is good. I think it's the best in the game right now. It usually charges per query. For example, if you run a SQL query on Snowflake with the same number of data records, it would take less than half the time compared to running it on Microsoft. It has good documentation. You can pick up Snowflake if you have previous knowledge of SQL.

I can only access Snowflake from the web. It would be better if we could have an app that we can install locally on our laptops to connect to the server without needing to go to the web page. Apart from that, it's hard to point out any limitations in the tool.

I have been working with the product for four years. 

The tool is scalable. I've used it for datasets with more than ten million records.

I only put data in and modify data. Most of the time, I don't require technical support. We occasionally had downtimes, and the data engineer would escalate these issues to Snowflake to resolve them.

The solution's deployment is simple. You purchase the license on the Internet—I think there's only a free trial for thirty days—and set it up like a Gmail account. It takes less than a minute to set up. You can set up your Snowflake server or use an enterprise vendor like AWS or Azure. Recently, Snowflake has been moving away from third-party vendors. They want to set up their remote infrastructure.

The tool's pricing is based on the number of queries you want on your database. The cost is small. To get the tool's pricing, you can do the math based on the cost per query, which is $0.002. If you're running your queries frequently, your charges will be higher than running fewer queries.

I would give Snowflake a ten out of ten in terms of performance and a nine out of ten in terms of scalability. I rate the overall solution a ten out of ten. 

We use the solution extensively for reporting and analytics. We put data into it and publish reports. A report could be a dashboard. The data is used for data-driven insights and AI and MLOps. There are many use cases for the data that we procure on Snowflake.

The product has impacted collaboration across our teams. The correlation is quite easy. We use the solution because most of our life sciences and healthcare customers prefer it. Their preferences directly impact us as vendors and partners. We have to promote the same tools and technology they are familiar with.

The product is quite fast. There are several connectors in Snowflake that we could leverage for ingesting data. The data could be in zettabytes or petabytes. It is easily manageable.

The cost is a bit high. The storage cost and the extraction cost of data are high. The final invoicing cost will be higher if we do a rich data-oriented task on Snowflake.

I have been using the solution for close to three years.

The tool is stable and very powerful.

We have at least 1000 users in our organization. We will surely use the product in the future.

The solution is cloud-based. There is no on-premise solution. It has its own cloud. The installation is quite easy.

The solution is worth the money. It is good to use. It has a lot of features which are not available in other products. However, users must explore other products to control their costs.

Other products like Google Cloud, AWS, and Azure have competitive pricing. Snowflake costs 15 to 20% more than AWS and Microsoft Azure.

People who want to buy the product must initially use the free trial version. It can be installed on a desktop. The user can do a lot of R&D. We have a good playground. We can play with the components and learn them easily. We can learn how each component talks to each other, how the performance is, and how to create tables. There are a ton of tasks we can do.

After ingesting the data, we can perform advanced analytics and make models. The end users can do many things on the tool. It is quite an easy tool. I will advise any new user to install a free version. They will be able to learn about the product and get good insights when they work on their pilot project. Overall, I rate the tool a seven 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.

The use cases are data warehousing, data pipelining, data engineering, reporting, and dashboarding. Instead of using Oracle or other systems, you can simply put Snowflake there. It works like a normal database. This way, you avoid consuming the system with extra data. So the experience so far is good.

One of Snowflake’s most valuable features is that you don't need to worry about computing. You don't need to scale up the compute size. When you are using a compute pay-per-use model and the storage is cheap, the data can go from terabytes to petabytes. In this case, you don't need to procure any new hardware or software, it automatically scales up.

I think the problem is maybe the design of the product. It is supposed to be a warehouse product, you can't expect it to be a transaction system. It is not designed for transaction systems. It's purposely built for data warehouses. So people cannot expect to use Snowflake for transaction systems. They misunderstand the product.

So I would not call it a product negative. I would say it's more of a user error, for example, people using a hammer for something else. But that's a problem. It works perfectly fine, otherwise.

They need to work on the design. That would suffice.

I have been using Snowflake day in and day out for four and a half years now.

It’s a stable product. I don't think there is any problem. I think the product is engineered well.

It is good at scaling. You can scale it horizontally or scale vertically. That's a big plus. Scaling easily is a big thing.

They are really good and responsive. I normally raise the ticket and they immediately come back with an email. They keep posting updates and follow-up promptly.

There is no setup issue at all. It's very smooth. You need to do a simple login. If you're doing the initial setup, it's maybe a couple of days of work. That's it. If you are migrating or want to do a proper new database setup, it's just a day's work.

We use Snowflake pretty heavily. We pay a significant amount of money for the tool. I'd say we pay $300k to $400K every year.

It's a great product. I think people don't need to stick to traditional Oracle systems and rather go for Snowflake. There you don't need to worry about any updates. It is a cloud product, so it automatically updates regularly. The advice is to get used to this way of working as it's new. You have to get familiar with the technology, and a little bit of the terminology. It's a very good product. I would rate it eight out of ten.

We use the solution as a data warehouse for our financial services firm. 

The core feature of the platform is everything works, and that's what I like about it. Our time to market is faster, it requires less maintenance, and I can build and deploy a product exceptionally quickly.

Everything is automatic, and I don't have to do any maintenance.

I want tokenization, so they could either acquire a company that does tokenization or somehow integrate with one. If I could do tokenization in line with other development without having a third-party system, that would ease integration and security, of course.

More data governance and access control features would be a welcome addition.

We have been using Snowflake for about three and a half years. 

Snowflake is a stable platform. 

The power of Snowflake is that it scales automatically and indefinitely. We have around 500 internal users using the solution daily, and most of our applications use the product in some shape or form, so that's a few hundred thousand external users.

The support model is that we have a Snowflake rep, and if I need anything, I can reach out to him, and he can get people on board within minutes. The support is fantastic.

Positive

We weren't satisfied with our data warehouse, AWS Redshift, Oracle, and some on-prem elements such as a SQL Server. We wanted a cloud data warehouse that didn't require a lot of manual intervention and maintenance, DBAs and so on. We wanted a solution that could scale automatically and pay-as-you-go to cut down on wasteful infrastructure. Therefore, Snowflake made a lot of sense, plus compared to Redshift at the time, the separation of storage and computing was huge. That was an essential differentiator for us.

We previously used ThoughtSpot, specifically their Falcon engine, their appliance version, and it did everything on its own. We brought in Snowflake later when ThoughtSpot introduced their product called Embrace. We were among the earliest adopters to switch, and six to eight months after, we integrated with Snowflake.

The initial setup was straightforward; it was one of the easiest I've done, so I rate the solution five out of five for ease of setup.

We carried out the deployment in-house, and Snowflake is a SaaS solution, so setup was rapid. All we needed was some user account information. 

Snowflake is expensive, but when I consider what we get for that price, it's fair. I rate the solution three out of five for affordability, right in the middle. 

I rate the solution nine out of ten. 

Snowflake is ahead of the competitors because it's completely automatic and hands-off in terms of maintenance. Many of the competitor products have similar features to Snowflake, but what they call automatic still requires someone to understand it. If they give us 100 levers, somebody has to know what each of them does and when to pull them, whereas Snowflake is entirely hands-off.

My advice to potential customers is to have a team member who understands performance tuning and to figure out optimal credit usage ahead of time to avoid wasteful spending.

The implementation is essential because the solution provides a lot of power out of the box, and the initial configuration needs to be fit for purpose. If I have a relatively small use case where I don't need much power or don't have much data, the product needs to be configured for that. As opposed to an external case where I might need high power for a government job, for example, then the configuration needs to be scaled up.

Snowflake is our centralized data warehouse. We bring all of our business critical information to Snowflake. It also powers all of our dashboard's reporting and analytical models.

Snowflake has improved our organization by powering company-wide dashboards that functions use to understand and manage their business. We also have dashboards that go up to our board of directors and our C-suite. Snowflake allows us to have up-to-date, consistent, and useful reporting for the entire company.

I found Snowflake's performance to be its most valuable feature. The product is an incredibly fast and performant data warehouse. We handle a lot of semi-structured data natively with it, which is nice. 

I also appreciate Snowflake's scalability. As the company has grown, as the number of Snowflake users have grown, I have not had any issue scaling.

There is room for improvement in Snowflake's integration with Python. We do a lot of SQL programming in Snowflake, but we go to a different tool to program when we have to in Python. However, I know this is an area that Snowflake's working on.

I have been using Snowflake for a year and a half at my current company. I have an additional six years of previous experience. 

Snowflake is incredibly stable. I have never had an issue with Snowflake outages.

Snowflake is scalable both in terms of the amount of data that you can run through it and the number of users that engage with it. We have five direct users and then, because Snowflake powers all the dashboards, 250 indirect users.

We plan to increase our Snowflake usage in the future. As we send more data through it and build out more sophisticated dashboards and reporting and especially statistical modeling, we will increase our usage.

I have not had a lot of issues with Snowflake, so I haven't had to utilize technical support that much. But when I have had issues, I received a response in a couple days, not the same day. The responses sometimes help solve the issue and sometimes not. The technical support could be better. 

Neutral

I did not previously use a different solution at my current company. 

The initial setup was very straightforward. 

Our employment was done in-house. In fact, I did it myself. 

We have seen a return on investment with Snowflake. 

We spend between $30,000 and $50,000 a year for Snowflake licensing.

I did not evaluate other options. I have been using Snowflake for so long and it has been so good for me to use that didn't need to.

I think Snowflake is fantastic for both storing data and querying your data, but you should always maintain visibility into your costs. They can run up and get out of control if you're not aware of who's using Snowflake and why.

We are currently using this product for data lake and data warehouse projects. Snowflake creates our repositories and enables the view from other tools like Docker and Power BI to create that data mesh. We use the data to create a process inside the warehouse. We are customers of Snowflake and I'm the senior manager of information technology infrastructure.

Snowflake is user-friendly and reliable in creating the data warehouse and carrying out data modeling. 

I'd like to see the data science functionality improved to a degree that would enable data scientists to work together with the data engineering team. The solution is focused on SQL and some kind of language or free support would be useful and would add some functionality to the machine-learning process. 

The current API is very limited and difficult to configure. It's not easy to create an API and start changing data so some kind of API to expose the data would be good. We use Docker to check the data but if we could get an API that allows other tools to access the data that would be great. 

I've been using this solution for two years. 

The scalability is good. There are around 35 people in the company using Snowflake and six engineers dealing with maintenance. 

The technical support is good. 

Positive

The initial setup was simple and took about a month. The main point is to align the data governance and integration process because you only need to click a button to start. We had a service engineer from Snowflake help with that.

If you use the tool well, you'll see ROI from your investment.

It's important to understand the licensing model because if you're paying for the software, you're not necessarily aware of the use. It's important to monitor how you're using the resources otherwise you can find yourself in a difficult situation. Licensing costs depend on the agreement you have with Snowflake.

I rate this solution nine out of 10. 

Data warehousing is typically a rich guys' toy. Large enterprises are only able to leverage data warehouses for data analytics purposes. We wanted to change that and wanted to build a data warehouse template model for businesses across industries. 

If Snowflake was not around, we would have used Google's Big Query or Amazon's Redshift, or a MYSQL/Postgres database in a Windows VM (virtual machine). However, Snowflake made it a lot easier for us with loads of features such as encryption of data in motion and at rest, masking policies, time travel (to correct data load issues), controlled access based on roles, data sharing, third-party data from marketplaces, etc.

It helped us to build MVP (minimum viable product) for our idea of building a data warehouse model for small businesses. 

About ten years ago, force.com from salesforce.com offered a similar platform for us to build data warehouses. However, our staff with a data engineering background found it easier to build the data warehouse in Snowflake, with the easy-to-use SQL interface and RBAC models (role-based access control). The platform saved us money as it automatically shuts down the compute engines after about five minutes of idle time. Per second billing (above the first minute) is great. 

In my view, cost optimization for the computing power required by the ETL jobs, reports, and dashboards is the most valuable feature. Especially for startups, this helps us to keep cost spending within control without having to worry about manually shutting down the server when not used.  

As a Google partner, we like to leverage GCP (Google Cloud Platform). Snowflake supports GCP, AWS & Azure platforms. This works just fine for us. Encryption of data with multiple keys for both data in transit and data at rest gives us enough confidence to use snowflake for our customer 360 solutions.

Currently, we use Snowsight only to monitor the usage of the Snowflake environment by our users. However, if Snowsight can be improved, we can host our BI (business intelligence) environment also within Snowflake. In our case, to provide basic reports and dashboards, we started to use Tableau, Power BI, Looker, and Qliksense, depending on our customer preference.    

We are yet to figure out how to integrate tools, such as Liquibase, to release changes to our data warehouse model. If Snowflake could guide us with some easy-to-use integration (similar to DBT integration), that would be great.

I've used the solution since 2020.

Stable

Scalable

Support can be enabled in the Snowflake UI.

Neutral

In the past, we used Google Cloud SQL. However, Snowflake offered cost optimization among the many other useful features. They also introduced app building on top of the data hosted. 

The initial setup is not difficult. Google Search will lead us to articles that can guide us on the setup of users, roles, warehouses, and access controls.

We did the initial setup on our own, and it was not difficult.

We constantly monitor the usage with grafana dashboards to keep the ROI growing and to assist/ alert users about any wastage.

Many interesting features are available only in the enterprise edition. Check out the differences when you are evaluating the product: https://docs.snowflake.com/en/...

We considered MySQL and Google Big Query. We're also happy with Google Big Query. 

Snowflake is growing with newer features and capabilities. But not much success with Stream lit app. Big query + app sheet is an alternative that we're considering.