Snowflake Reviews

We have day-in and day-out data coming from our various heterogeneous source systems. We read from that and we load data into our target tables, or rather data lake and data warehouse that is built into Snowflake. We can even translate and delete items. Apart from that, we use the tasks and schedule jobs within Snowflake. Once the data is generated into the final data material, we share it with the client for their review.

Time travel is one feature that really helps us out. If there is some data discrepancy or something wrong that happens with the table, at any point, we can get that data back and time travel via fail-safe features. They're really good. 

The feasibility to increase horizontal or vertical scaling is great. It really helps a developer at an enterprise level. If you're considering scaling, it won't take much time. 

We can create cloning via Snowflake. There's a lot of time savings.

The initial setup is simple. 

The solution could use a little bit more UI. Something was in process, however, it's not yet deployed or in the version that I'm using it is not deployed. If we have to use any table or any schema in other DB features, the prompt comes over after a dot. Whenever we are using it it would really help if it looked better. 

If you're running a procedure, it just gives a standard error instead of the exact error captured. If you have to look at it, you need to go into the history to look into which query failed, and we need to figure it out. Instead, if the error, whatever it is, is instead displayed in the history, showing the point of failure, it would be more visible. It would save some time for all the people who are using it.

There are some reporting analytics that we can use. I'm aware of those. However, if there was more reporting, we'd appreciate that. We'd like for it to become a complete one-stop-shop solution. 

We'd like to have some automation around small tasks, especially around scheduling.

I've been working on the solution for two years. 

In the past three years, I have faced instability. In just one or two instances, I would say when we were running the queries, it was not fetching any result and the error was not proper. That said, that happened in just one or two instances, in the lower environments. Otherwise, the product is very stable.

The solution is quite scalable both horizontally and vertically. It's an easy job. We just need to resize it. 

Within our module, there are around eight to ten people who are working on Snowflake. In a full project, if I'm looking at the holistic picture, there are around 30 to 40 people who are using Snowflake.

We've reached out to technical support in the past. In some cases, we were not even able to fetch the data. Or, a couple of times, in the time traveler, the pay phase time was expired and we had to retrieve that data. When that happened, we had to reach out to Snowflake support. In the first instance, they said there was downtime at that server, which we were not informed about. Since it was in the lower environment, it didn't cause an issue. 

Overall, the technical support was really good. They were on it. They helped as much as they could. For a few things they needed some approvals, and they took that. They got back to us in even less time than was required by the SLA.

Positive

We are also working with Teradata.

The main difference is that Snowflake is on a cloud server and Teradata is still a legacy system. Teradata was one of our source systems. We were loading it into Snowflake. With Teradata, when you create primary care, something constrains the way you want to do it so that you do not put an additional effort into your ETL. That changes when it comes to Snowflake. If you're clustering something on a case, obviously you end up paying the extra cost. That is one basic and main difference that we saw.

That said, you can process a huge amount of data in Snowflake. This becomes a challenge in Teradata. When we were reading the data, we had to load that data into Snowflake. When we were trying to read that data, we had to obviously divide it into chunks and then load it into Snowflake. Loading into Snowflake was like a cakewalk. Everything is moving into the cloud now. Legacy systems like Teradata just can't handle the amount of data required. 

I've worked on many of the legacy systems like Oracle, DB2, et cetera. Migration from one environment to a higher environment was a huge thing. The DVS used to take two or three days sometimes depending on how many tables or what the objects are and what they had to create. Now, with the cloning feature that we have, the idle time for the developers, or even the people who are looking at the data, the analysts, and everyone, is reduced to none. We just clone it, we load the data and the data is available without any hindrances.

The solution is straightforward. 

My Azure ID was created by the DBA. As soon as I had to log in, my user role was set up. To log in to the interface hardly took me more than two or three minutes. Compared to our Data Bricks, it was pretty simple. It's hassle-free. 

The maintenance of the DBs and the production services are normally taken care of by the team itself who's handling the production support. However, just in case, if there is something, if a huge mishap happens, which we cannot recover, or we would need Snowflake customer support help, that is when the help is taken. Otherwise, the team handles everything in-house. 

The majority of the cases that we have seen are some queries stuck somewhere and out of the blue. In the case of some source data, it decides to send you a huge amount of data on a particular day. While we have seen some mishaps, a regular case requires our developers to deal with it.

We handled the initial setup in-house. 

The pricing is fair for all the features that Snowflake is providing. The way Snowflake has emerged in the past few years is impressive. It was just a beginner in the space, and it has become a leader in the market. For all the features it provides at a basic cost, it is impressive.

There are no extra costs involved unless you need to scale and grow and need more in terms of size. When we started, a medium-size was sufficient. We'd since needed more. 

The solution allows us to suspend our warehouse as well if we would like. 

That said, even if you're paying a little more cost for the data safety that you have, you will ensure that there is no data loss that will happen. It's very impressive.

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. 

We are completely migrating to Snowflake, and we are in transition. It is primarily to combine all our data repositories into a single place. We have SAP BW and SAP HANA, and some of our business units have their own databases. We chose Snowflake to consolidate all of our data into a single place and then build enterprise data. We are then going to provide the data for our businesses in shared databases, on which they would do reporting. They will also have the ability to bring in their own data, which is currently not possible. They will also be able to do advanced analytics, machine learning, and AI in Snowflake, which is not fully possible on our current platforms. It will be used for all the operational reporting, such as sales, supply chain, appraising, and merchandising. We just started to do reporting related to sales and supply chain inventory.

We have its latest version. It is currently deployed on Amazon AWS, but we are moving to Google.

There are so many features that Snowflake offers to address the challenges that people have been facing in the traditional data warehouses for a long time. It allows us to have a single repository for all the data. Currently, we have data repositories all over the place, and we want to bring everyone onto one platform so that it can be utilized across the organization. Currently, we need database administrators and SAP administrators to manage multiple databases and platforms. With Snowflake, we don't need any admin, and there is zero maintenance. All we need is a platform architect who can just manage the Snowflake platform to create databases and security roles, and then you can share the data. By integrating everything into a single Snowflake platform, we have lowered the total cost of ownership quite a bit.

The ability to share the data and the ability to scale up and down easily are the most valuable features. The concept of data sharing and data plumbing made it very easy to provide and share data. The ability to refresh your Dev or QA just by doing a clone is also valuable. It has the dynamic scale up and scale down feature. 

Development and deployment are much easier as compared to other platforms where you have to go through a lot of stuff. With a tool like DBT, you can do modeling and transformation within a single tool and deploy to Snowflake. It provides continuous deployment and continuous integration abilities.

There is a separation of storage and compute, so you only get charged for your usage. You only pay for what you use. When we share the data downstream with business partners, we can specifically create compute for them, and we can charge back the business.

They need to incorporate some basic OLAP capabilities in the backend or at the database level. Currently, it is purely a database. They call it purely a data warehouse for the cloud. Currently, just like any database, we have to calculate all the KPIs in the front-end tools. The same KPIs again need to be calculated in Snowflake. It would be very helpful if they can include some OLAP features. This will bring efficiency because we will be able to create the KPIs within Snowflake itself and then publish them to multiple front-end tools. We won't have to recreate the same in each project. 

There should be the ability to automate raised queries, which is currently not possible. There should also be something for Exception Aggregation and things like that.

I have been using this solution for two years.

It is all cloud. It is really stable. We haven't seen any problems.

We can scale up or down based on our needs. We don't have tons and tons of data, but based on the quality feedback from our vendors, it can handle large volumes and has the competency. With the dynamic scale-up feature, we are confident that it is going to meet all our requirements.

Currently, our number of users is very limited because we have just started the migration. We don't have many users on the platform. All of our focus is on Snowflake because we're moving to Snowflake, and its usage will increase in the future.

I do not directly interact with the support, but I believe our platform architect reached out, and he got a response.

We had SAP BW and SAP HANA as our main data platforms. We are slowly decommissioning SAP BW and SAP HANA and completely migrating to Snowflake. We wanted to have a single repository for all the data. The cost was also a factor.

It is straightforward. To expose the data in the cloud, we had to go through our info security and legal, so that's the part that took time. After that is done, the process for setting up the platform, getting signed up with the initial free credits, and signing up the licensing for the credits was straightforward.

We are working with a system integrator or vendor for this project. Our strategy is to work with an experienced vendor for the first project, and after that, we would be able to drive things forward.

Our experience with them is good. They're building the architecture of Snowflake. They have experience, and we have our own thoughts. We are working together and making sure that the architecture is for the long-term and not just for one project. Whenever we see that their focus is limited to the project, we are asking them questions to make sure that they are making the right decision.

In terms of maintenance, it doesn't require any maintenance, but you do require architects. We have three architects. One architect is responsible for the platform and takes care of creating security rules, grants, and users. We also have an integration architect who is responsible for data acquisition, ETL, and stuff like that. We have a data architect who is responsible for the overall data architecture in terms of what layers we need to establish and how do we model the data and publish that for consumption.

There is a separation of storage and compute, so you only pay for what you use. 

The key part is skill set because Snowflake is all SQL-driven data warehousing. Internally, we have some SAP BW development resources, and they need to learn and move on to understanding SQL-based coding and custom data warehousing concepts.

I would rate Snowflake a nine out of ten.

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.

It is used in my company as well as in my client's company. We are a system integrator, so naturally, we need to have the centers of excellence and competencies in Snowflake.

The way it is built and designed is valuable. The way the shared model is built and the way it exploits the power of the cloud is very good. Certain features related to administration and management, akin to Oracle Flashback and all that, are very important for modern-day administration and management.

It is also good in terms of managing and improving performance, indexing, and partitioning. It is sort of completely automated. Everything is essentially under the hood, and the engine takes care of it all. As a data warehouse on the cloud, Snowflake stands strong on its ground even though each of the cloud providers has its own data warehouse, such as Redshift for AWS or Synapse for Azure.

There are three things that came to my notice. I am not very sure whether they have already done it. The first one is very specific to the virtual data warehouse. Snowflake might want to offer industry-specific models for the data warehouse. Snowflake is a very strong product with credit. For a typical retail industry, such as the pharma industry, if it can get into the functional space as well, it will be a big shot in their arm.

The second thing is related to the migration from other data warehouses to Snowflake. They can make the migration a little bit more seamless and easy. It should be compatible, well-structured, and well-governed. Many enterprises have huge impetus and urgency to move to Snowflake from their existing data warehouse, so, naturally, this is an area that is critical.

The third thing is related to the capability of dealing with relational and dimensional structures. It is not that friendly with relational structures. Snowflake is more friendly with the dimensional structure or the data masks, which is characteristic of a Kimball model. It is very difficult to be savvy and friendly with both structures because these structures are different and address different kinds of needs. One is manipulation-heavy, and the other one is read-heavy or analysis-heavy. One is for heavy or frequent changes and amendments, and the other one is for frequent reads. One is flat, and the other one is distributed. There are fundamental differences between these two structures. If I were to consider Snowflake as a silver bullet, it should be equally savvy on both ends, which I don't think is the case. Maybe the product has grown and scaled up from where it was.

I have been using this solution for close to three years. I kept a tab on Snowflake and its progress since it came into the market.

Personally, I have worked extensively with Oracle, SQL Server, and Teradata. SQL Server has the Fast Track Data Warehouse (FTDW) appliance. Oracle has both the database and the appliance. I haven't worked on Parallel Data Warehouse, which is a big one offered by Oracle. Teradata is an appliance in itself. There is also Metadata. I haven't worked on DB2. 

All of these had their own lacunae. Data warehouses had their own problems. There were failures, challenges, and difficulties in adoption, and all of these have been addressed by Snowflake a big way. It has tried to marry the best of both worlds in terms of turnaround time, scalability, adoption, and seamlessness.

I hail from a classical data warehouse background. Snowflake has been kind of a silver bullet. It is trying to meet the best of both worlds. I wish I could do much more on Snowflake, but I'm tied up with many other things, which is why I'm not able to concentrate that much, but it is an exceptionally good technology.

Its initial setup is very simple, which is its plus point. It is not at all a problem. You only need to understand a bit of the cloud ecosystem. When Snowflake is on Azure or AWS, you need to understand

• What exactly is happening?
• How these two are handshaking with each other?
• What part Snowflake is playing?
• How Azure or AWS is complementing it?

If these things are clear, the rest shouldn't be a problem.

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.

We are a consulting company so our primary use depends on the niche that we are providing the services to and on which of the different versions they have. I think we are mainly using Snowflake Enterprise.

In general, it is being used for integrating information. Snowflake is a database platform, it gives information to support analytic needs, such as advanced data analytics like machine learning. In some of those cases it is also used for descriptive analytics, for instance BI.

One of example of how Snowflake has improved a client's organization is the democratization, it makes information available to more of the users.

The features that I have found most valuable are the ease of use, the rapidness, how quickly the solution can be implemented, and of course that it's been very easy to move from the on-premise world to the Cloud world because Snowflake is based on SQL also.

I think that the area of improvement with Snowflake is to improve the administration. It would benefit from an administration that allows you to be aware of your credit consumption once you have the service so that you may be sure how many credits you are consuming when you use the platform and to make sure that you are making the most efficient use of these resources. In other words, to improve their interface so that you may monitor the consumption of your credits on Cloud.

I also heard from a company we work for that it could be more user-friendly because it provides some tools but they are not user-friendly.

Additionally, it would be very helpful if Snowflake integrated machine learning and some other advanced analytics features within their language or product capabilities. Right now, they do it through some other company where you have to buy these capabilities from other vendors. There are some customers that don't have complex needs for machine learning or advanced analytics so they don't have to buy it from another vendor but can use it from the product itself if they have it.

The whole company has been using Snowflake for about three years.

In terms of stability, so far it is very stable.

Snowflake is very scalable. Our client companies where we implement Snowflake are medium to large sized. These companies have offices in different parts of the world, not just some regions, but companies with office users in different parts of the world. We are dealing with international companies. Their tendency is to increase the use of the Snowflake platform. It would serve all the analytical needs in these companies.

I have not directly experienced the technical support. It's not part of my job to be involved on those kind of issues, but we constantly receive information as a partner from them and we are very in good touch with them and with the people we are working with, meaning the representatives that are within the Latin American market, which is where I work. They are very open and very fast with communication.

The initial setup is easy. Full deployment takes a few weeks. The initial deployment for the first initiatives might take weeks. It's not complex, really. You may have it loaded after a full day and already providing results or interacting, but there are some other companies that have to be implemented to extract and consume the information from the database. But it's very easy.

There have been a couple of other solutions that we've been participating in the evaluation process of and some others that have been included in the decision process, including Redshift from AWS and also Azure Synapse from Microsoft.

For instance, AWS Redshift looked like it was easier to implement and to be adopted by the technical users, the programmers and database programmers. So far it has been far easier to adapt this technology. I'm not saying that AWS is a better technology. It's very complex, but at least what I've seen is that for them, it looks like it's been easier to use the first time.

We liked that Snowflake is able to be used as a multi-Cloud service - it can be used in AWS Cloud, Azure Cloud, or Google Cloud. Whereas AWS, or even Synapse, can only be used in their corresponding networks.

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 have been working on Redshift, Snowflake, and AWS RDS Oracle. In the particular case of RDS Oracle, they were migrating from on-prem Solaris equipment to cloud-based RDS.

I would suggest Snowflake for anyone with the need for a reporting/business analytics view of their data that wants only wishes to maintain technical FTE's around processing the data into or out of a data repository but, doesn't want to go to extent of technical management of "AWS clusters" for the data repository.

It was relatively easy to use, and it was easy for people to convert to it. Moved 168 tables and appropriate indices to Snowflake with minimum modification to Current Oracle DDL. The largest degree of change was setting up the corresponding access Hierarchy to duplicate what was in Oracle ( customer had separate permission structures for application vs Admin/support vs direct reporting access to the data).

The aspect of it that was more complicated was stored procedures. It does not support SQL language-based stored procedures. You have to write in JavaScript. If they supported SQL language and stored procedures, it would make migration from on-prem much simpler. In most cases, if an on-prem solution has stored procedures, they're usually written in SQL. They're not written as what most on-prem DBMS would refer to as an external stored procedure, which is what these feel like to most people because they're written in a language outside of SQL.

The other thing that people found difficult to deal with was that they had several Oracle DBAs who were very experienced DBAs, but they were used to on-prem. They were used to having the ability to turn any dial and flip any switch. Moving to Snowflake did cause some issues there because they had to completely readdress the fact that they couldn't touch the engine, and they had to spend more time analyzing performance.

I probably used it about six months ago. I haven't been working with a client who is currently on this platform.

I haven't had to call on them for a problem at that level.

It was a cakewalk. The biggest thing that's hard to do with it is that you have to do an analysis of performance over time to determine the scale because they separate compute and storage.

Scaling the query to a proper size compute is the larger aspect of the problem for most people. That's because you're looking at something completely different. The problem is that you're now trying to figure out what is the largest compute you need to keep performance where you want it without going too large. If you were in an on-prem scenario, you would tweak and twaddle all the dials. You might rewrite the query, but at the end of the day, you're still working inside the same physical acquisition or same physical resources, whereas in Snowflake, you're literally saying that you've got a 10 million row table as part of your query, but what is the necessary compute facility that you need to run queries that are running against that table.

It is hard to say because we're usually engaged in the transition as opposed to the long term. Their storage costs are easily within pennies of what AWS S3 would normally cost. 

Most of the clients I've been working with are in the financial sector, and they're relatively small. I would put them in an SMB connection. The first thing we have to bring up for people is that they're going to build this. They shouldn't store their data in S3. They should pipeline directly into Snowflake and use it on their storage. So, the cost is a big issue because these are small to medium size companies, and that is the biggest thing we had to price point for them.

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.

I have used it in my previous company. It was just a SQL server data warehouse using reporting tools on top of it. It was an on-premise SQL server environment, and it was a typical data warehouse use case, but we wanted to do things faster and more cost-effectively. 

We used it to modernize our data warehouse. We didn't want to invest more in on-premise servers, and we were looking for a way to quickly get more data joined together. 

It had definitely improved the way our organization functioned at the time.

The overall ecosystem was easy to manage. Given that we weren't a very highly technical group, it was preferable to other things we looked at because it could do all of the cloud tunings. It can tune your data warehouse to an appropriate size for controlled billing, resume and sleep functions, and all such things. It was much more simple than doing native Azure or AWS development. 

It was stable, and their support was also perfect. It was also very easy to deploy. It was one of those rare times where they did exactly what they said they could do.

Their strategy is just to leverage what you've got and put Snowflake in the middle. It does work well with other tools. You have to buy a separate reporting tool and a separate data loading tool, whereas, in some platforms, these tools are baked in. In the long-term, they'll need to add more direct partnerships to the ecosystem so that it's not like adding on tools around Snowflake to make it work. They can also consider including Snowflake native reporting tools versus partnering with other reporting tools. It would kind of change where they sit in the market.

I have been using this solution for about three years.

We didn't run into anything. We had outages for a couple of seconds, but they were related to Amazon or AWS. They weren't related to Snowflake.

We scaled it a little bit. We didn't have a lot of data to scale, as a lot of companies do. We only had a couple of terabytes of data, which is insignificant for a cloud platform. 

The development team had three or four people getting data in. Then report people were also using the platform, but they didn't really have to know that it was Snowflake because they were going at it through a reporting tool. There were probably 30 or 40 people writing queries against our reporting tools, which were, in turn, using Snowflake.

They were really good. They were very responsive. There were never any issues with them. I would give them a ten out of ten.

I've used a lot of different data warehousing solutions at different companies.

It was easy as pie. In a couple of hours, it was up and running, and we were loading the data in. We had a fairly senior developer for that. He knew SQL server and queries very well. If you're used to developing in any type of SQL environment, you can jump in and use Snowflake really quickly.

It is per credit. It has a use-it-as-you-go model. We bought a chunk of 20,000 credits, and they were lasting us for at least a year. We didn't have the scale of data like a much larger company to consume more credits. For us, it was very inexpensive.

Their strategy is just to leverage what you've got and put Snowflake in the middle. It doesn't make it expensive because most of the organizations already have reporting tools. Now, if you were starting from scratch, it might be cheaper to go a different way.

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.