Snowflake Reviews

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.

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 a solutions architect for Snowflake.

The Time Travel feature is helpful for accessing historical data and the ability to clone external tables is useful.

I would like to see more transparency in data processing, ATLs, and compute areas - which should give more comfort to the end-users. 

Improvement to the end-to-end process of loading data into Snowflake could be made as well.

The product is stable so far.

The product is scalable enough.

The initial setup is not complex.

I believe that pricing is reasonable for this solution.

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.

We use Snowflake for data warehouse modeling and reports.

Once you have finished your designs they can be easily imported to Snowflake and the information can be readily accessed without an IT expert.

The documentation could improve. They should provide architecture information.

There could be better integration with tools other than the common databases used to receive data. There are other tools that have ETL tools within, such as Tableau. You are able to work with information prior to sending it to Tableau. This feature would be nice to have in a tool from Snowflake.

In a future release, they should make it easier to do reporting. A drag and drop type feature would be good. If not a drag and drop feature, there should be some other easier way to do it than it is now.

I have been using Snowflake for approximately six months.

The experience that we have had until now is that we can use the Snowflake very well from the videos on the web. The knowledge that our company already has regarding this solution has helped. We are producing some very sophisticated solutions. There is plenty of material on the web that you would be able to have lessons and learn.

We have worked a lot with Tableau previously.

We deploy the solution on-premises because we are developers, the customer is the one who has it on the Cloud. We helped them with the on-premises deployment and then we install the software and we deployed our solutions made on-premises. We complete any changes that need to be done in order to work in the customer's landscape.

The time of the deployment depends on the solution the customer requires. If it's a small solution, typically it will take approximately two weeks. A medium solution, that takes from two weeks to eight weeks.  However, it depends on what you are trying to accomplish with the solution. If you are trying to do a very complex data warehouse, it's not the tool that times the most time, it's the analysis and design that takes the most time for deployment. Once that you have the analysis, design, and you transport them to Snowflake this is not difficult.

In any BI solution, you have a lot of changes because of what you need to do with the end-users, there are a lot of changes to the end-user. This can also take up some time for the deployment for the first time. It can take two to six weeks for a medium-sized project.

On average a small project can take three people. That's in small BI projects, in some customers that we have the project takes a maximum of six weeks in order to have all the data fields. This is not for a whole data warehouse but for sales and customers. Those are all small to medium-sized projects, that require three people maximum for deployment. You might always want to have in addition, an analyst and the senior architect. 

Most of our team are technicians.

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.

We use the solution for a data warehouse and we generating reports and dashboards.

Snowflake has improved my organization because of its high performance compared to the old way we used to operate with Microsoft SQL Server. We are migrating everything from SQL Server to Snowflake. It used to take a lot of time to query the database but now it is done a lot faster, we receive millions and billions of reports. This is a major benefit because it is our major use case.

The most valuable features are sharing data, Time Travel, Zero Copy Cloning, performance, and speed.

The solution is very easy to run the queries. We have a built-in query optimizer in Snowflake that works very well.

The UI could improve because sometimes in the security query the UI freezes. We then have to close the window and restart.

There should be an IDE concept similar to the Java IDE or Eclipse feature. I should be able to see all of the functions available on a particular object. Every time we need to go to the Snowflake documentation and look if there are any methods we need. It is hard to remember everything, go and search, and use that that eventually found method. If it was possible to list out all the methods and functions available in an object that would help the developer's a lot.

In an upcoming release, we should be able to send or receive data from external systems but this is not able to be done. There should be built-in logging and monitoring features, we should not need to be dependant on third-party solutions, such as Splunk. There should be more DevOps features to reduce the usage of third-party tools. If these features were part of Snowflake it would be a fully functional complete solution.

I have been using Snowflake for approximately two and a half years. 

They claim zero maintenance support and from my experience, I would agree with that statement. When I was on a previous project we had a lot of support for the Netezza platform we were using. We had approximately twelve people, three onsite and seven offshore. When we migrated from Netezza to Snowflake we reduced the number of people required and kept only some of the team as developers. There is very little support required for this solution. Stability is very good in SnowFlake.

The scalability is built into this solution as being on the cloud. It is able to scale in all directions. Additionally, they have a multi-cluster warehouse, and based on the business use case it is very good.

There are approximately 4,000 portals. However, we do not know how many users our clients have that are using their portals.

We are building new data warehouses and we are migrating from SQL Server to Snowflake.

The support is very good. We create tickets and they respond with a solution.

We were using SQL Server previously and we switched because of the increased performance, multi-clustered shared environment, scalability, and we wanted to use a cloud-based solution.

Everything with the installation went smoothly. I believe when I joined the company Snowflake was already here. They bought the Business Edition that is encrypted everywhere because they are a financial insurance company and most of them choose the Business Edition because of the security.

The company I work for used SnowFlake integrators for implementation assistance.

I have evaluated Eclipse and IBM Netezza.

The solution is very easy and flexible to integrate with any type of API.

I rate Snowflake a nine out of ten.

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.

The primary use case for Snowflake is in our data warehouse project. We have a private DW and whoever has the credentials can access it. I am a data integration developer and we are using ETL tools to extract the data from different source systems and then load it in the data warehouse.

Snowflake is the latest technology. It has great flexibility whenever we are loading data and performs ELT (extract, load, transform) techniques instead of ETL.

This solution automatically performs micro-partitioning when the data is loaded. This creates a dynamic partition and based on the cluster, the performance is fast and really impressive.

Snowflake is using MPP, massively parallel processing techniques, which is a great feature. It saves developers time and allows us to focus more on client requirements.

It is difficult in some cases to perform ETL and this is something that should be included. As it is now, I use Informatica PowerCenter to load data from on-premises to the Snowflake cloud-based data warehouse. If this could be done by Snowflake directly, without an external integration tool, then it would become a full package. It would be awesome.

They do have a native connector to connect with integration tools for loading data, but it would be much better to have the functionality built-in. We would like to be able to just write an SQL query and do our work.

I have been working with Snowflake for six months.

We have not had any major issues with stability.

Snowflake does not require manual scaling because it does it for you. Developers just need to load the data and process the query. That's it. The developer's job is not to spend time improving performance, as it was with an on-premises solution. We had to do the partitioning, collect the stats, and everything else. In the case of this cloud-based solution, it doesn't require as much work. Instead, we can focus on the queries.

We are planning to increase our usage of Snowflake.

I have been in contact with technical support many times and it was awesome. I got great support. Whenever I needed anything they were ready to help me out, which was nice.

I have experience with Informatica PowerCenter and Oracle. PowerCenter uses ETL techniques instead of ELT. Oracle does not automatically perform micro-partitioning. Instead, you have to partition manually and it is a static partition.

Prior to Snowflake, I was using an on-premises data warehouse. Snowflake is the first experience I have had with a cloud-based data warehouse. It is an awesome tool.

There is no need to install this software, which is the best part. It doesn't require any maintenance, and although DBA support is required, it is much less compared to an on-premises solution. This type of cloud-based solution has no requirement for software, hardware, or maintenance because everything is managed by Snowflake's system.

The only assistance I had during the setup was from the integration tool, Informatica PowerCenter. This was used to export our on-premises data from Oracle and import it to Snowflake on the cloud.

You pay based on the data that you are storing in the data warehouse and there are no maintenance costs.

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.

We needed a data warehouse and we made a decision on what is the right tool for us as a data warehousing tool by comparing products. We looked into Microsoft Azure, Red Shift and Snowflake. In the end, we decided on Snowflake because it looks more up to date, it seemed much better purposed as a data cloud solution.

It was developed from scratch and dedicated to being used on the cloud and that was what we were looking for. It was not just an on-premises system which was then converted to use on the cloud. It was completely developed from scratch and purely focus on the cloud.

Because it was programmed with that dedication, it has some significant advantages.

The thing I find most valuable is that scalability, space storage, and computing power is separate. When you scale up, it is live from one second to the next — constantly available as you scale — so there is no downtime or interruption of services.

It has something like a time machine, as it is from Apple it incorporates that feature in a way similar to their operating system. So whenever you need a version of the data to test with, you can just go back and take a copy of what was backed up yesterday. It makes some things very easy. It backs up your data warehouses, so for example in our case, a colleague deleted a complete database and we just need to do an undrop on the database and the data was there again.

This helps you to have a development environment with current data. You can just clone your production environment and you have a development environment. Everything you do you can test it on real production data without destroying the production data itself.

These are significant advantages.

The company is constantly working to improve the product. Now they have a focus on data sharing, which is really great. We already share data with others who do not have Snowflake. That alone is already great. But if the other counterparts also have Snowflake, then it is extremely easy to share data. You can control access at low levels and even on the cell level. It is very secure.

With the improvements they continue to make, there is nothing now that I would say I miss or features that need to be added. Maybe there could be some more connectors to other systems, but this is what they are constantly developing anyway.

We have been using this product for two years.

The product is very stable. We never had an issue with stability. It is reliable and it is extremely fast. For example, we had a stock procedure that took half an hour to complete on our SQL cluster, and in Snowflake it was running in two minutes. So that is a significant time savings for just one task.

The number of people at our company currently using the solution depends on what we are trying to accomplish. We have four developers in Snowflake and then we also have users who are leaving data with us for our further analysis. That may be around ten other users.

With the growing data set we have and the increase in the size of our business, we will increase the use of Snowflake, but not with respect to the number of users. We are a small company and all the users who need to use it are already using it. We have more data that we need to load and which we want to integrate before we will make more usage of Snowflake.

There is nothing for us to complain about when it comes to technical support. The response time is really great. Whenever we have an issue there is some delay because they are in San Francisco in the United States so there is a time difference. But when we raise an issue, we get answers immediately. We may not get the solution immediately, as that is not always possible. But we get some type of immediate response and days later we have a solution. The tech support is quite responsive.

We use several products together for our framework. We have our data warehouse which is in Snowflake, we use Domo for standard reporting and we use R for data science analysis.

Before we had Snowflake we had a different solution. We switched to Snowflake because we felt the need to modernize our data warehouse architecture. We were also thinking about having other solutions in the cloud to reduce administration costs. With no effort on our part, we could have a stronger system compared to the effort and cost of doing a similar thing on-premises. This was the biggest advantage of Snowflake. We really do not need to have those administrative efforts anymore. Now we don't take care about when we run out of storage or that we need to buy better CPUs because if we need more computing power, we don't worry about it, we just use it and it is there.

The setup for the product was straightforward. For us, it was a little bit of a challenge because when we implemented the data warehouse, we also changed the architectural concept and we implemented a better framework. Because this framework was new to us it complicated our installation. But Snowflake itself, if you want to use and you have a data warehouse already in place with the right framework, then it is straightforward. You just store your data in and that's it. What you use on top is material for orchestrating all the load jobs. But this is other integrations and other choices that are really outside Snowflake itself.

The initial deployment from purchase until it was up and running in production took two months.

We had a consulting company help us for the initial two months of the setup and then afterward we did everything by ourselves. We were quite satisfied working with the consultants and they helped us to implement quickly. We mainly needed them because we implemented this metadata framework. In the beginning, we had this consultancy for analyzing our platform, which to select and which tools should be used. After we completed this initial portion of the project over the two months, we needed them mainly for completing the implementation of the metadata framework.

Snowflake itself is easy to learn. If you know SQL it is really not very hard. Everything is well documented and it is not a problem.

The whole licensing system is based on credit points. That means you commit to using it and you pay for what you use. You can also make a license agreement with the company so that you buy credit points and then you use them. So if you buy credit points that you think will last you for a year, you pay a certain amount of money and then you have these credit points available. What you do not use in one year can be carried over to the next year and it is that easy. The advantage of buying more is that you get a discount when you buy a bigger package with more credits.

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.