What advice do you have for others considering 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 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.

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.

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.

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.

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.

I would give Snowflake a seven out of ten.

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

I give Snowflake a nine out of ten. Snowflake is being used at the enterprise level. If an organization is interested in embarking on its cloud journey and Snowflake fulfills its requirements, I would recommend it as a viable solution.

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.

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

I give the solution an eight out of ten. My advice is to start Snowflake and not spend too much time thinking about how we could use the solution or what it could be used for. The key is just getting started.

I give the solution an eight out of ten. Deciding between Azure Synapse and Snowflake can be difficult, as the best choice depends on one's own use case. Ultimately, it comes down to the available connectors; the product with more connectors is likely the better option. When making a decision, one should consider which other sources they would want to get data from and where they want to send data to. This can help inform their product selection.

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.

Snowflake is very useful as a data lake and as a data warehouse. Also, it has a lot of features with respect to data science. We are not there yet, but if there are any specific use cases around compute, data distribution, and data sharing, then Snowflake is a tool to be considered. I'd rate Snowflake a seven out of ten.

I would recommend this solution to others. I rate Snowflake an eight out of ten.

I rate this solution nine out of 10.

Snowflake is probably the best data warehouse solution on the market. AWS is playing catch up with S3 Data Lake and Redshift AR3 but they are nowhere close to where SF is. 

Of course, SF's true compute and storage capabilities make it unique. Being able to scale query processing power using SQL within milliseconds is brilliant, no other company can do that. This means I can in my SQL code, invoke a warehouse unit (CPU power) to process some heavy SQL, then invoke a smaller warehouse unit when done. 
These warehouse units are sleeping power, they can be awakened by SQL, do their job, then go back to sleep a few seconds after you're done. That is unprecedented.

Back in time feature, (being able to select a query AS OF (up to 90 days) is also awesome. 

Another big point is SF uses S3 as storage layer, so why not use it for your data lake. The price of storage is similar to S3 (unless you turn on retentions for back in time) still at $45/month/ TB compressed, that's a great deal. 

Nothing prevents you from archiving data into Glacier and bringing it back into S3 which SF can ingest via Snowpipe.

Finally, the Data Marketplace platform they're trying to build is going to revolutionize how we share data. 

There is a big reason why investors poured billions into the IPO for SF. 

Snowflake is an amazing Product. It is one of the best Warehouses currently in for Cloud. Separation of store and compute and the Warehouse concept makes this unique and it has lots of features, low maintenance and the cost can be optimised to a great extent if we understand how Snowflake works under the hood. 

Other great feature is the way it handles semi-structured data like JSON and XML which is unique. So we can use it as a data lake if required (keeping in mind to not over complicate or misuse it )

For a data warehouse project, there is nothing on the market today that can compete with SnowFlake. TeraData is trying to play catchup, and AWS Redshift trying to come up with an hybrid. 
Snowflake is already years ahead with Data Share, allow you companies to share a their database or schema from their data warehouse with another account. Think DMV data warehouse sharing their driver profiles with FBI data warehouse, and the data is accessible within your account. What just happened? Elimination of data pipeline. No need to transfer data. You just join : select from dvm.driver_profile JOIN fbi.criminal_profile on ...
Snowflake is also building a "Data Marketplace" where you can bring in your data warehouse "data set", without any import.

Being able to rollback up to 90 days, (undrop table, database) being able to clone a DB in few seconds, etc ...

The list if very long. 

Snowflake is not for real time analytics though. I would suggest SingleStore instead. 

Advice 1 : If the organization has more than 60 to 70% of data science or ML use cases, I would recommend to them to use Azure data bricks instead of Snowflake.

Advice 2: If the organization is heavily into business applications and does not have much ML systems and contains standard reporting, Snowflake is a good choice.

Advice 3: There are other cloud data warehouses in the market (Firebolt) which can be looked by customers to gauge the long term OPEX and ROI in comparison to snowflake.

SnowFlake is useful to merge all kinds of data, no bother if you have heterogeneous or dis-paired data sources. Try to check every requirement to connect with the data warehouses and be careful with the data federation and cost of resources.

I would recommend the SaaS version for their organization. It is not complicated to use. Establishing a private link with current cloud services has been challenging so I would recommend having some kind of a block. I would rate this solution a nine out of ten.

I would rate this solution 8 out of 10.

My past company was a Snowflake customer. This current company is not, however, it may be on the verge of it. I'm using the latest version of the solution. We switched clouds at some point. I was using it on AWS, and now it is on Azure. I'd recommend the solution to others. I would rate it nine out of ten.

I would rate it a 10 out of 10.

I rate Snowflake an eight out of ten.

I would rate it an eight out of 10.

Snowflake is easy for business users to implement, allowing them to start small and grow big. I think people are still debating on whether they should still continue with S3 and add Snowflake or, whether they should take away S3 and completely rely on Snowflake. I would rate this solution an eight out of ten.

The biggest conversion problem we've seen so far is when someone had a large number of stored procedures that were SQL-based, as opposed to external stored procedures written in C or whatever the DBMS would support. Converting those stored procedures either to a SQL script or to a stored procedure or function that's based on JavaScript is the biggest challenge that most people we've dealt with are having. That's because they have to relearn the language they're writing their logic in. I would easily rate it an eight out of 10.

Snowflake has a lot of capabilities and performance. However, the tool is not a silver bullet and can do everything. If you designed what you need according to the tool, then everything is going to be okay. This is true for any tool. Many people start the projects without validating what they are going to expect to have at the end, they receive a big surprise. They were thinking that the tool has this capability and it doesn't have it or perhaps it has the capability but the design you have does not work correctly. If you see the percentage of projects in the different customers in many places, such as in Mexico, Florida, and Miami. Snowflake is a tool that is currently being used but has not been in the past. There is not a lot of history. I rate Snowflake a six out of ten. We have not used Snowflake long enough to better rate it. If we had a lot more formal education or had more information or reference manuals our experience would be better.

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.

My advice to those wanting to implement Snowflake is it is easy. However, the way to choose to implement your data in the warehouse matters. When we started to implement our data with Snowflake, we also switched to a metadata-driven approach, but the method depends on the people involved in the implementation. Overall, the implementation of Snowflake follows similar principles as any other data-warehouse implementation except many aspects are a lot easier and helpful. I rate Snowflake an eight out of ten.

One of the concerns related to Snowflake is about longevity in terms of how long can we use Snowflake. It is a big question in the market. It is a new baby in the market, and we don't know for how long will it trend. It has some big competitors. Firebolt claims to be the number one in this area. They have much better features than Snowflake. I would not say that Snowflake is the best and in the right position at this point in time. Snowflake is good for the next year, but Firebolt is going to bring it down. I would rate Snowflake an eight out of 10.

The solution is very easy and flexible to integrate with any type of API. I rate Snowflake a nine out of ten.

We're customers and end-users. We're using the latest version of the solution. I can't speak to the exact version number. I'd rate the solution at a nine out of ten. We've been mostly very happy with its capabilities. I'd recommend the solution to other users and companies.

It is a good solution. At the moment, I can't find another product that is better than Snowflake, but it needs better ETL functionality, easier configuration, and cheaper support. All products have got some limitation. I would rate Snowflake an eight out of ten.

On a scale from one to ten, I would give Snowflake an eight.

This could be something that might be debated upon, but Snowflake has two parts to it. One is the data warehouse itself, and the other one is the cloud. It is important to know about the cloud in terms of: * How a cloud functions? * How a cloud orchestrates through its services, domains, invocation of services, and other things? * How a cloud is laid out? For example, let's take AWS. If AWS is invoking Lambda or something else, how will S3 come into the picture? Is there a role of DynamoDB? If you're using DynamoDB, how would you use it in the Snowflake landscape? So, cloud nuances are involved when we speak of Snowflake, and there is no doubt about that, but a more important area on which Snowflake consultants need to focus on is the core data warehousing and BI principles. This is where I feel the genesis of Snowflake has happened. It is the data warehouse on the cloud, and it addresses the challenges that on-prem databases had in the past, such as scalability, turnaround times, reusability, adoption, and cost, but the genesis, principles, and tenets of data warehousing are still sacrosanct and hold good. Therefore, you need the knowledge or background of what a data warehouse is expected to be, be it any school of thought such as Inmon school, a Kimball school, or a mix. You should know: * Data warehouse as a discipline. * The reason why it was born. * The expectations out of it in the past. * The current expectations. * What being on the cloud would solve? These things on the data warehouse side need to be crystal clear. The cloud part is important, but it is of lesser essence than the data warehouse part. That's what I see, personally, and I guess that's the way the Snowflake founders have built the product. As a data warehouse, I would rate Snowflake an eight out of ten.

I would recommend this solution to others and we are going to keep using the solution in the future. I rate Snowflake an eight out of ten.

We are a direct customer and end-user. We've been using the solution during a POC for the last year or so. It's a pilot project to test its feasibility for our company. We're just starting to get performance stats and stuff like that. I'm not sure which version of the solution we are currently using. I don't recall the exact version number. Usually, people are running the latest version. Whatever the latest available option is is likely the number we are on. I'd rate the solution at an eight out of ten. We're still in the POC phase, however, based on what we have seen, we are quite satisfied.

We don't recommend a cloud typically. We leave it to the customer. If the customer has AWS, and then we sell Snowflake on AWS. If the customer has Google, we sell Snowflake on Google or Azure, or if they have Azure, we sell Azure. They may even have a private cloud. We don't force the customer to buy a particular cloud. We go with the customer's preference from their cloud services because Snowflake works on all three clouds. In general, I would rate the solution eight out of ten. It does what it says it will, and I have yet to hear of customers complaining about its capabilities.

We are implementors of the solution. We are using previous versions of the solution. It may not necessarily be the latest version all the time. I'd advise other organizations to try it out and play with it a bit to see if it would fit their needs. Overall, I would rate the solution at a nine out of ten. We've been mostly very happy with it.

I would advise looking at your environment. Look at the workload and what you are trying to migrate. There is no one size fits all model. If you are a transaction system and you want to go with Snowflake, I would not advise this solution. If you are a reporting system and you want to migrate, Snowflake is the best choice. You also need to look at what kind of queries people are running. Don't assume that just because you are moving to Snowflake, you are going to cut down the cost by some factor. That is not going to happen. You need to really do a lot of homework and groundwork to know what kind of queries you're running and how can you avoid the compute costs. There is a lot of metadata available in Snowflake. You have to look at all that and then consciously try to improve the numbers. It is definitely a good tool and a good database without any adoption problems. Users who are SQL savvy can immediately adopt this solution. User onboarding is not really a huge exercise. It is a very simple exercise. I would rate Snowflake an eight out of ten.

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.

I would recommend this solution to others. We plan to keep using it. I would rate Snowflake a nine out of ten.

My advice is to consider Snowflake when you have more customers. I wouldn't consider Snowflake until I have sufficient customers. Whether we will consider Snowflake in the future depends on how BigQuery behaves. If the cost of BigQuery starts increasing and becomes similar to Snowflake, we're going to switch. If not, we're going to remain with BigQuery. We might also consider other similar solutions, such as Yellowbrick, or switch to another cloud solution, such as Azure or AWS, depending on the price. Right now, we are paying about $2,000 per month. Our goal is to have the total cost of everything to be around $3,000 per month. It is more or less our goal for KPI kind of thing. I would rate Snowflake an eight out of ten.

If time to value is your primary goal, then I would recommend going for Snowflake over one of the other cloud providers. I would rate Snowflake a ten out of ten. It is one of the few products in which everything demos well. It actually did everything they showed in the demos. We really couldn't find any gotchas in it. It kind of delivered as promised.

I would definitely recommend Snowflake. On a scale of one to ten, I would give Snowflake an eight. I give it an eight out of 10 due to its room for improvement in the user interface for the monitoring of the credit consumption and that the user experience is not friendly. And also because the machine learning is lacking some advanced analytic features.

I would recommend this solution. I would rate Snowflake an eight out of ten.

We're partners with Snowflake. We've been partners for just under a year at this point. I'd definitely recommend the product. It's worked quite well for us. A new customer needs to understand, however, that they need a roadmap of at least five years when they are deciding on their data warehouse. They should compare costs and sizing to make sure they are getting the solution that makes sense for their current and future needs. The solution integrates well with other applications, and if you need it to integrate with existing applications, you still should check to make sure it's possible. I wouldn't necessarily recommend Azure over Snowflake, as they aren't really a good comparison. Snowflake is more focused on data repositories and data warehouses. AWS does give you many options, however.

Personally, if I have the choice, I would rather recommend Snowflake to my clients over a product from Microsoft, for example. They have some overlapping functionality, but they also have some separate stuff. Snowflake does not have the size to develop at that pace but personally, I find them a more sympathetic company than Microsoft. I would rate this solution an eight out of ten.

I would rate Snowflake an eight out of ten.

The best advice I would give is to push for a POC. Pick a couple of use cases where you think you could quickly get value and just see how quickly you can get it implemented. One of the key features of Snowflake is that you can get it up and running straight away.

I would definitely recommend this solution. I would rate this solution a nine out of 10.

My advice for anybody who is implementing Snowflake is to start small, then prove out the value and you can grow. I would rate this solution an eight out of ten.

My advice for anybody who is considering Snowflake is that it is a really good product, especially if you are having issues with Big Data. It is not good for a typical OLTP environment, such as a small table. I would rate this solution an eight out of ten.

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.

My advice for anybody who is considering implementing Snowflake is that from a user's standpoint, it is a good product. Having a database in a cloud setup means that you don't have to scale and it has got many features already included. For our use case, we found this Snowflake was good enough and did not need any enhancements. I recommend using it. I would rate this solution a ten out of ten.

We're a customer. We don't have a business relationship with the solution. Users considering adding the solution should understand that Snowflake can be used only for transactional processing, not for analytical processing. If they want to go for transaction processing, they can go for Snowflake and if they want to go for analytical processing, they should look at or go for an Oracle database. I'd rate the solution seven out of ten.

We're partners with Snowflake. The difference between Snowflake, and, for example, Azure, is that there is real separation between the computer and storage. Snowflake is the only one that's really separate and it's much simpler to scale or shift data. It makes everything much easier. One of the best options on the market right now is to have a cloud-only setup. Not everyone is using the cloud, but everyone will catch up. I'd rate the solution nine out of ten. If the user interface was better and they had a few more features and did some useability tweaks it would be perfect. Also, it's hard to get the data out of Snowflake, and that's a real issue design-wise.

Analyze your user scenario. If your scenario is managing large amounts of data in extremely, different environments in a structured way, then this is a good option. I would rate this solution as an eight (out of 10).

This is an awesome tool, but there is room for improvement in terms of integration. Also, in terms of management, nothing is perfect and everything can be improved. I would rate this solution an eight out of ten.

My advice to someone who is looking at Snowflake is that if they are looking at analytics tools in addition to warehousing and certain advantages of cloud computing, then I would suggest Snowflake. But if they are just looking for the warehousing part where you will later just use another solution on top of it, then I would not suggest Snowflake. On a scale from one to ten where one is the worst and ten is the best, I would rate Snowflake as an eight or nine. Let's say 8.5.

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

The solution is deployed on the cloud using Azure, where there's a data warehouse. We primarily use SQL scripts. For those considering implementing, I'd advise that they understand the business very deeply first. Not every business would have a demand for Snowflake, so it's not for everyone. It's important to understand the requirements and then, if it makes sense, to implement Snowflake. I'd rate the solution eight out of ten. I'd rate it higher if it had a better user interface.

We are system integrators, so for all our customers, we offer different solutions. We only use the cloud deployment model. Snowflake doesn't offer on-premises deployments. Snowflake on cloud is the best right now. There are only a few other options. Redshift is not scalable. With SQL Data Warehouse the concurrency is an issue, as well as scalability. Also, it does not have all the features that you see in an on-premises SQL Server. Snowflake a good database. I'd recommend the solution to others. I'd rate the solution seven out of ten. We are still new to the solution. There are a few things we still have to explore before we would give it 10. Typically, I come from a Hadoop background, so compared to Hadoop I think everything looks good before the data warehouse side. We're quite pleased with Snowflake and moving from Hadoop into Snowflake has been a very good transformation.

I would suggest being careful with selecting resources. Each customer case can be completely different and each can require different resources. It's not only the database itself but also how you integrate it with the analytics and the resources. The estimation of the resources is something that you have to pay a lot of attention to when selecting the resources because sometimes you will need EPL or ELT integration, which requires a tool, as does analytics. For an end to end solution, you have to include other products. I would rate this solution an eight out of ten.

Because most of the issues you come across can be dealt with on the user's sites, it's important to educate the users and understand their requirements. The best advice I can give is to understand the product and to try to stick to what is required. From the business side, you need to monitor usage and monitor the space because of on-premises constraints. If it gets filled up then you will have to react. However, this solution is very scalable. I would rate this solution between seven and eight out of ten. The solution still has some constraints that need to be addressed.