The main use cases for Snowflake Data Cloud are standard data warehouses.

Real-time data sharing capabilities are not something we require with Snowflake Data Cloud.

It's easy to use, which is a feature that really helps me out.

Snowflake Data Cloud is scalable enough for my data management processes.

The reduced infrastructure management is the big difference compared to on-prem warehousing with Snowflake Data Cloud.

Snowflake Data Cloud is good enough security-wise for my needs, and it can integrate in terms of networks and user management.

Integration with third-party tools is possible, not just Snowflake Data Cloud solutions.

Pricing is quite high for Snowflake Data Cloud, which is an area that could be improved.

Snowflake Data Cloud is still beneficial to use, but only if you can afford it.

It can be cost-effective if you're using Snowflake Data Cloud at an enterprise-level business.

I've been working with Snowflake Data Cloud for at least four years.

The deployment of Snowflake Data Cloud was a quite smooth process.

I have not contacted Snowflake Data Cloud technical support.

I've had no need for it, with no questions or issues.

In a couple of weeks, you can have a fully enterprise architecture with Snowflake Data Cloud.

I have not worked with anything else in the cloud as competition to Snowflake Data Cloud.

I worked with on-prem warehousing before Snowflake Data Cloud.

Snowflake Data Cloud is good, and I can recommend it to colleagues or friends, though it may depend on the use case.

On a scale where ten is a perfect solution and one is absolutely useless, I would rate Snowflake Data Cloud an eight out of ten.

I primarily use Snowflake for hosting and analyzing data. It acts as a data warehouse where data is stored, analyzed, and moved from stage to stage, ultimately exposing it to end users. Additionally, there is an increasing trend in implementing AI capabilities, allowing me to write SQL queries for insights into structured and unstructured data.

The independence of the compute and storage within Snowflake is key. The integration with third-party solutions like DBT, Airflow, and Fivetran is highly beneficial. The scalability options it provides, addressing issues without tying workloads into one virtual machine, enhance functionality. The fast pace of delivering new AI features also brings excitement about future possibilities. Further, being able to perform AI and Machine Learning in the same location as the data is quite advantageous.

There is a need for a tool to help me estimate the cost of using Snowflake. Enhancements in user experience for data observability and quality checks would be beneficial, as these tasks currently require SQL coding, which might be challenging for some users. It would also help if Snowflake provided clear guidelines on how requests impact warehouse size.

I began using Snowflake in 2021.

Snowflake as a SaaS offering means that maintenance isn't an issue for me, and I have not experienced any cases where it was down.

While Snowflake provides the ability to scale resources, the expected return on investment is not always achieved. The billing doubles with size increase, but processing does not necessarily speed up accordingly.

The technical support from Snowflake is very good, nice, and efficient. I rate it ten out of ten.

Positive

Setting up Snowflake in 2021 was challenging, especially due to the strong security requirements at the enterprise level. It involved back-and-forth communication with Snowflake and Azure support. However, the documentation has improved over time, which would likely streamline the process now.

I assume I achieve a certain level of return on investment, though I am skeptical about the calculations. However, I am generally happy after adopting Snowflake.

It is complicated to understand how requests impact warehouse size. Unlike competitors such as Microsoft and Databricks, Snowflake lacks transparency in estimating resource usage.

Snowflake's main competitor is Databricks. Databricks was initially built for big data and machine learning, and then moved to SQL capabilities, while Snowflake followed the opposite trajectory.

New users should not proceed on their own without leveraging the experience of others who have already implemented Snowflake. Establishing a framework for operation and change management is crucial. Define a clear operating model for Snowflake adoption, and start with a small warehouse to adjust as needed. I rate Snowflake a 9.5 out of ten because there is room for expecting further improvements.

Public Cloud

Microsoft Azure

I started working with Snowflake when I was with Fidelity Investments around 2016-2017. We used Snowflake on AWS cloud because Snowflake doesn’t have an on-premise offering. You need to use it with AWS, Azure, or Google Cloud. 

As a consultant now, I assist enterprise customers, though I don't have Snowflake deployments yet.

Snowflake is a data lake on the cloud where all processing happens in memory, resulting in very fast query responses. One key feature is the separation of compute and storage, which eliminates storage limitations. 

It also has tools for migrating data from legacy databases like Oracle. Its stability and efficiency enhance performance greatly. Tools in the AI/ML marketplace are readily available without needing development.

Cost reduction is one area I would like Snowflake to improve. The product is not very cheap, and a reduction in costs would be appreciated.

Snowflake is very stable, especially when used with AWS. It works best with AWS compared to Google Cloud and Azure.

Snowflake is very scalable and has a dedicated team constantly improving the product. There are no problems on the scalability side.

Snowflake's technical support is excellent. During my time at Fidelity, I received great support in migrating data to Snowflake, with quick responses and innovative solutions.

Positive

The initial setup was rated six out of ten due to the time required for migrating existing data to Snowflake. Configuration and data migration are major steps involved.

Snowflake's pricing is on the higher side, rated as eight out of ten. If there were ways to reduce costs, it would be a positive improvement.

Snowflake is a great solution if you have substantial data volume. For those considering Snowflake, be prepared for the necessary initial investment in time and resources. 

I rate the overall solution nine out of ten.

Public Cloud

Amazon Web Services (AWS)

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.

Our company uses the solution for building a data platform, data warehouse, and data transformation.

The product is somewhat used for data analytics, but it is mostly for data engineering.

The tool is good for handling large datasets, and since the tool is fully managed by Snowflake, you can scale up the compute part.

I don't think that the AI tools in Snowflake are good. AI tools in Snowflake can be improved. Even if the AI tools in Snowflake are good, I feel that it would be expensive. The cost of the AI part does not justify what you get from the product.

The price of the product can be lowered.

I think Snowflake should integrate with some tools like ChatGPT.

I have been using Snowflake for a year.

The product is scalable and can be considered a good fit for small and medium businesses.

I haven't directly contacted the technical support team of the product.

I have used Azure Databricks and Azure Data Factory. My company decided to use Snowflake since we wanted to be able to get up and running fast without much configuration-related mess. Snowflake doesn't give you the options with the configuration part since, by default, it is available out of the box. In terms of machine learning, Azure Databricks has the upper hand over other products.

The product's deployment phase was quite okay.

The solution can be deployed in a few days or up to a week.

The product's price range falls between average to a bit expensive range. I think the tool is worth the money if you use it properly. It is difficult for me to speak about the number of users who use the product. My company pays around a couple of thousand dollars a month to 10,000 dollars or more.

I think the main benefit is that with the tool, you can easily get things going without problems since you don't need to configure all the parameters manually. If you buy the tool for a bigger computing purpose, the engineer can pay more attention to the tool, and I guess after that, you can do more with the solution. I would ask others not to think about the data warehouses, as Snowflake takes care of such areas.

The benefits from the use of the product can be realized in around 40 minutes. It is a good technology for getting up and running quickly.

Snowflake is integrated with Azure Data Platform and other ETL tools in our company's ecosystem.

The integration capabilities of the product are good and you get what you pay for when it comes to Snowflake.

I rate the tool a seven to eight out of ten.

The solution has use cases related to retail stores and sales.

The most valuable features of Snowflake are that you have to pay per usage, and you don't have to worry about the maintenance of the data warehouse because it is on the cloud.

The solution’s pricing could be cheaper. It would be helpful if Snowflake could create good reports instead of using Power BI reports.

I rate the solution a nine out of ten for stability.

Snowflake is a scalable solution. We have four to five customers for Snowflake who use it regularly.

The solution’s initial setup is straightforward.

The solution's deployment in a development environment takes only a couple of minutes.

Users have to pay a licensing fee for the solution, which is expensive.

Snowflake is deployed on the cloud. The solution is providing HIPAA compliance, which is sufficient. Users looking for a pay-as-you-use product available on Azure or AWS should consider Snowflake.

Overall, I rate the solution an eight out of ten.

We use Snowflake as a database to store all the data we stream from the source system.

The best thing about Snowflake is its flexibility in changing warehouse sizes or computational power. If I want to change the computation with other tools like Netezza, I would have to add a hard disk to it. With Snowflake, being on the AWS side, changing warehouses provides me with faster execution of my queries.

The real-time streaming feature is limited with Snowflake and could be improved.

Currently, Snowflake doesn't support unstructured data. With Snowflake, you need to be very particular about the type of data in your source systems. It has to be in a proper structure. You cannot push data to Snowflake in any possible way.

I have been using Snowflake for a couple of years.

Snowflake is a stable solution.

Around 400 users are using the solution in our organization.

We previously used IBM Netezza. We switched to Snowflake in 2020 because it provided us control over its scalability and costing model.

The solution’s initial setup is comparatively easier.

Snowflake is a cloud-based, scalable solution that provides strong data security. Handling all the data is much easier in Snowflake, and it has a very nice interface to control user access. The administration of Snowflake is also comparatively easier.

Snowflake is a cost-effective solution.

Choosing Snowflake completely depends on the quantum of data your organization has and the requirements. Snowflake is suitable for someone looking for a scalable and cost-effective solution that provides quick analysis.

Overall, I rate Snowflake a nine out of ten.

Public Cloud

Amazon Web Services (AWS)

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 solution is primarily used for data warehousing. 

It's a good tool. It is quite easy to manage. 

There are a lot of features. 

If you accidentally delete something, you can time travel and retrieve it. 

Within half an hour or even 15 minutes, you can set up a new data warehouse, and you can clone a table or whatever you like. 

It's easy to provide access to clients if they need to.

The solution is stable. 

It can scale well.

There is good online documentation via the community, and you can learn the solution on your own. 

These days, they are pushing users towards the GUI or graphical version. However, I am more familiar with the classic version. I'd like to continue to work with it using the older approach. That's just a personal choice. I prefer the database views like you would get on an SQL Server or other databases. 

I've been using the solution for two and a half years or so. I haven't used it for too long. 

The solution is stable and reliable. There are no bugs or glitches, and it doesn't crash or freeze. I've never had an issue when you connect it to your infrastructure. I'd rate the stability nine out of ten. 

We have 250 or more users on the solution currently. They are predominantly IT specialists, including engineers and developers. It's for those on the application side of development. The solution is scalable. I'd rate the scalability nine out of ten.

I've never directly dealt with technical support. We do have a direct contact. There are other people who might have. There might have been a version issue at some point. However, it wasn't something I dealt with. I've only interacted with the Snowflake community pages. 

We are currently moving everything from Oracle to Snowflake. 

We've used a combination of other tools in the past. We've used Microsoft's Stack, for example. We were using a SQL database in the past. 

We're migrating from Oracle right now. We have two people that handle the maintenance of the solution. A company may need two to four engineers to manage maintenance 24/7.

It's not difficult to deploy. It's pretty straightforward. It took us only a couple of minutes in order to get everything up and running. 

We pay a licensing fee to use the solution. It's paid monthly. My understanding is that it is not that expensive. We might have a specific extra cost surrounding running it on a private cloud. I'd rate the cost as six out of ten. It's moderately priced compared to others on the market. That said, I don't directly manage the account and don't directly deal with pricing. We have a contract of three to five years. 

I'm an end-user. 

Everything is on the cloud, and therefore I'm always using the latest version of the solution. It updates itself regularly. 

It fulfills our needs, and it's easy to pick up by looking at some reference guides. I'm still getting used to the GUI. I might find it even easier to use if I go through more formal training. Right now, I've simply learned it on my own. Overall, I'd rate the solution eight out of ten. 

Private Cloud

Amazon Web Services (AWS)

We use the solution for data storage and integration purposes.

The solution's most valuable feature is path application.

They should improve the performance of the solution's transaction application.

It is a stable solution.

We have 20-30 solution users in our organization.

The solution's customer service is good.

The solution's initial setup was very straightforward.

The price for the solution's license depends on the use cases.

I recommend the solution to others and rate it as a nine. It efficiently processes the data and gets reports as well.

Hybrid Cloud