Snowflake Reviews

We are silver or gold partners. The main use case is that we are building a data lake. We are creating a couple of downstream applications as well that will be used by data scientists. So, we will have a single data lake that will be used across the organization by different business domain users. The data is multi-source. We have data from SAP, JDE, and some Excel files.

The speed of data loading and being able to quickly create the environment are most valuable. 

For data, it provides built-in security and compliance with different standards, such as SOC 2, ISO, etc. So, we don't have to do a separate audit for compliance.

There are some gray areas. For example, there is no clarity on where the data sits exactly. That is their proprietary information, and they are not sharing those details. 

Its price should be improved. On the cost-side, it is more expensive than others.

If they could bring in some tools for data integration, it would be really great.

I have been using this solution for almost two and a half years.

It is pretty stable. Its stability is excellent.

It is scalable. As of now, there are 500 users, but slowly, we are planning to roll out to multiple regions. It is currently in Europe, and we will be rolling it out to the APAC and USA regions. By the year-end, there will be more than 1,000 users.

They're perfect. They're excellent. It could be because we are partners.

It is straightforward. It is not that complex.

Our own team deploys it for customers, but the initial configuration is done only by the Snowflake team because that is their area. 

I have worked with multiple clouds, and cost-wise, it is a bit costlier than others, such as Redshift. Its price should be reduced.

I would rate it an eight out of 10.

We use it as a traditional data warehousing application that we then set all of our reporting tools on top of.

We are able to consolidate multiple databases into one unified table for more complete reporting. That wasn't possible in our legacy tool that we were using because the query time was just too long. Now we're able to create this unified view of our entire organization and refresh it every 15 minutes; using the power of Snowflake's query is pretty much our biggest use case there.

The query speed, and the way that it actually executes its queries is the most valuable aspect of the solution. We had some queries that would take hours upon hours to run, and the Snowflake returns the results in about 15 minutes.

It requires no maintenance on our part. They handle all that. The speed is phenomenal. The pricing isn't really anything more than what you would be paying for a SQL server license or another tool to execute the same thing. We have zero maintenance on our side to do anything and the speed at which it performs queries and loads the data is amazing. It handles unstructured data extremely well, too. So, if the data is in a JSON array or an XML, it handles that super well.

One area for improvement would be the stored procedures. Currently, their stored procedures can only be executed at a transactional level versus being able to run and do updates and run things in a sequence.

An additional feature I'd like to see is called materialized views, which can speed up some run times. I'd like it to be able to be used where you can have multiple tables inside them; materialized view. That would be nice. As well as being able to run cursors, to be able to do some bulk updates and some more advanced querying, table building on the fly.

I've been using Snowflake for about four years now.

The stability has been phenomenal up until lately. We haven't had any issues until the last month. For the four years prior it was always on; we didn't have any outages. All in all the stability is great. The availability is extremely high. There's just been something in the last month that has caused outages for some periods of hours.

It's definitely scalable. We're on a very small usage compared to some of the other clients I know Snowflake has, so it's definitely scalable because we have tons of room to grow for our use.

Including myself, we currently have five users and they're data analysts.

I've only used their customer service in one or two instances, and they were very supportive and helpful. The tool is so user-friendly and straightforward that I've never really had to engage their professional services.

We didn't really have a traditional data warehouse application. We were just using Microsoft SQL Server, but we didn't actually have a traditional MPP-based data warehouse solution. We were still a very growing organization. As we continue to grow our business and increase in size, we have to get better tools that are meant to actually do what we're trying to do with other tools.

The initial setup was pretty straightforward. The permissioning is a little more complicated than it needs to be. It would be nice if it just assumed permissions when you create new tables or new users, but you do have to go and actually permission to everything for individuals and people rather than when you create something. It's just because there's no default role that applies to new stuff created so it's a little more complicated than it should be.

Our deployment took about one month. I'm the only one involved in the maintenance of the solution now.

We hired an ETL specialist to come in and get us set up, but he really didn't understand our business and what we were trying to accomplish. So everything he did, we pretty much paid for and then redid ourselves. But it was pretty straightforward using tools that are built for ETL processes. Understanding the SnowSQL command line tool to a certain degree also helps.

We don't really have it commercialized or revenue-generating in any way, but what we've seen with it is we've been able to remove all of our reporting and other data needs off of production application. So we're not putting extra stress on things that we need to always have up and running in order to operate the business. That's really our security. It's more of a favorite blanket if you will, is where we're seeing the benefits.

For our licensing, we renew every January by $25,000 in both credits.

Their pricing structure is a pay-per-second usage in terms of credits, but you can get discounts if you buy them in bulk. I think it's $1.10 an hour in terms of usage. We just buy upfront and that gets us taken care of for the whole year.

We did. I evaluated Google BigQuery and Amazon Redshift.

In terms of distinguishing features between each of them, it was really just two things. One was the speed factor of query times. The other thing that really sold us on Snowflake was their ability for data sharing. They have a unique product as part of their solution that you can share information directly with other individuals, either in their own additional private cloud or if they're not Snowflake customers, simply sharing a URL link to where they can receive data themselves.

It's good to use every day. It's the backbone of our entire reporting platform for both internal and external deployments of reports and visibility. We plan on continuing to grow our usage with it, as we put more and more people into our reporting platforms and bring our customers into more self-service that's going to increase the usage of the tool by the way that it actually serves up the information to the BI platform.

It's not at this time a transactional sort of database solution. It's truly only meant for data warehousing or data laking, and there's a lot of different ways to do role-level security. So you've got to have a good plan on that, but if you're looking for it to be the backbone of a transactional application, it's not the right tool for that.

I would rate this solution a ten out of ten.

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. 

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.

Snowflake is used for data warehousing.

The most valuable features of Snowflake are its performance and power.

Snowflake could improve if they had an Operational Data Store(ODS) space.

I have been using Snowflake for approximately four years.

The stability of Snowflake is good.

The scalability of Snowflake is very good.

I have approximately six customers using this solution but in addition, I am directing more.

The technical support from Snowflake was poor previously, they have improved significantly.

Snowflake has an easy setup and it is quick.

There is a license needed to use this solution. There are a few licensing options available. They have a pay-as-you-go option, but it is recommended to pay upfront.

I would recommend this solution to others.

I rate Snowflake a nine out of ten.

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.

I am using Snowflake for migrating data and table backups.

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

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

I have been using Snowflake for approximately three years.

The stability of Snowflake is good.

Snowflake is a scalable solution.

We have approximately 200 to 300 people using the solution.

The initial setup of Snowflake is easy.

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

I rate Snowflake an eight out of ten.

We implement this solution for our customers. It is a cloud data warehouse. It is SaaS, and it can be run on Azure, AWS, or something else. We are using its latest version.

It is a very good platform. It can handle structured and semi-structured data, and it can be used for your data warehouse or data lake. It can load and deal with any data that you have. It can extract data from an on-premises database or a website and make it available in the cloud.

It has very fast implementation and integration as compared to other solutions. There is no need for the DBA to manage or do the day-to-day DBA tasks, which is one of the greatest things about it.

In future releases, it can also support full unstructured data.

We have been using this solution for a year.

It is stable.

It has very good scalability. Your data can grow in the platform. We have at least 50 users of this solution in an organization.

Their vendor is wonderful. I only have good words for them.

It is not too complex. Its implementation is easy even for those people who don't know Snowflake and are coming from other environments, such as Oracle or SQL Server.

It can be implemented very quickly. Our customers in Israel implemented it very quickly. It was much faster to implement than other platforms.

It is on a monthly basis. It is based on your usage. There are no additional costs from the point of the licensing fee.

We do give some kind of evaluation to the customers about how much it is going to be. You can decide in Snowflake the virtual machine that you are using for customers. There are several kinds of virtual machines that you can use. It is similar to the clothing sizes: small to extra large. If you need more power in the coming month, you can decide in advance and take a more powerful machine. You can just select it from the platform. You can also decide which machine you want to take for extracting data.

I would advise others to check themselves how fast its implementation can be and how responsive it is. I would also recommend evaluating it before choosing other solutions, such as Microsoft Synapse or Amazon Redshift. You can test it yourself by using a test case. You can try to load the data on each platform, which can take a few weeks, but you will get to know the advantages of this solution. It is very different from other solutions.

I would rate Snowflake a nine out of ten.