Snowflake Reviews

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. 

Our primary use case for Snowflake is inputting data generated by AWS.

This solution has helped our organization by being easy to maintain and having good technical support.

The features I have found most valuable are the options to connect with extendable sources in three buckets in which we can also create stages.

I think that Snowflake could improve its user interface. The current one is not interactive.

I have been using this solution for about one year.

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

I would rate the scalability of this solution a 10, on a scale from one to 10, with one being the worst and 10 being the best. There are around five developers in our company and 500 end users for this solution.

We previously worked in AWS.

I would rate the initial setup process an eight, on a scale from one to 10, with one being the worst and 10 being the best.

I would advise other people trying to use this solution to build a skill balance as it's quite difficult to work in Snowflake.

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

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.

We're ingesting third-party data analytics into a database held within Snowflake. We have pre-production and production environments with integration to staging and production schemas.

It has improved the way our organization functions. However, we're still pretty elementary in our understanding of how it all works and the complete capabilities of Snowflake.

The adaptation to development languages is most valuable. Our developers can SQL code or something else. It has been convenient in that regard.

The data sharing capabilities across business units within the organization should be better. There could also be better integration.

I have been using Snowflake for a year and three months.

We have high confidence in it.

We have high confidence in its scalability. In terms of its users, for our solution, we only have a team of 10, but we have plans to increase its usage.

We haven't had any technical contact. All of it has been internal for our organization.

This is a net-new solution. So, it's brand new. We chose Snowflake for a variety of reasons, but mainly, we chose it for its scalability and data sharing capabilities.

I would rate it a three out of five in terms of complexity just because we didn't have any Snowflake developers that were available. The implementation took about three months.

We implemented it on our own.

We have not yet seen an ROI.

We're based on credits. So, we're paying four and a half dollars of credit. There are no additional costs. I would rate it a two out of five in terms of pricing.

I would advise ensuring that you have the expertise with domain knowledge in Snowflake. The time from initial concept to deployment could be expedited extremely fast. Just from our internal learnings, we see that our time to production has increased month over month.

I would rate it a six out of ten just because we're unaware or naive to the full capabilities of the product. However, I would highly recommend it in terms of setting up data warehousing internally over an Azure solution, such as Synapse, or something else.

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.

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 work with multiple customers who were asking for this and other similar solutions. We've since had several team members certified in Snowflake and we have a certified team working with that solution and keeping up to date with developments. I'm the general manager of the company and we are implementers. 

Snowflake has its own features in comparison to other similar solutions like Exadata. This gives it huge competitive power. It has a very good interface tool with its own benefits and features which are really useful for our needs from a computation and storage perspective. I think this solution provides the best potential of any data warehousing product where they choose to use Snowflake instead of Oracle or DBII. The product can be leveraged with respect to better performance, auto tuning, competition and performance. From an architectural perspective, the solution has all the ingredients it requires. 

We've come to realize that for many customers, pricing of this solution is an issue. Maintaining Snowflake clusters is challenging and cost intensive. Reporting could also be improved. Any data that moves out of Snowflake is being cached. If I have 400 to 500 end users, with 100 or 200 reports on a daily basis, all the reports will be cached. It's a matter of ensuring that costs can be optimized. The combination of Red Warescape plus Snowflake is a combination from the design and development perspective. But the combination from the reporting perspective to micro strategy on top of Snowflake could be a better feature, so there's a combination that has to be considered.

I've been using this solution for about 10 months. 

Our customers tell us that it is a stable solution. 

We're working in 32 countries with enterprise size customers. We're still waiting to see what happens with that level of scalability and how the solution performs. If this product can be further fine-tuned or optimized in such a way that it becomes a very good fit for the Azure platform, that would be great. 

They provide very good documentation on implementation design and development perspectives. 

I think the initial setup is straightforward. Anyone who has worked on these types of solutions will pick it up quickly.

It really depends on the nature of the implementation. If it's a small or medium sized company, we focus more on the pricing. If that can be brought down, I think Snowflake has a high potential that it can meet and can create a big name for itself in the big data cloud implementation platform. It has all the features. It already has all the complementary features to deal with the challenges. Those are built in and taken care of. It could be on Google cloud, or it could be on Azure or it could be on Amazon.

I'll rate this solution a nine out of 10. 

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.