Snowflake Reviews

We are also using Apigee we have various consumption patterns, data enrichment, and few shedding of the data, and everything goes into Snowflake. If it is multiple consumers, it goes into AMQ, Kafka, or multiple streams to consume. There are specific APIs that we offer after we send the data into the S3 bucket. We have Apigee APIs for consumption, and there are three to four different patterns. For example, we enrich the data, flatten it, and structure everything before the customers going to go into Snowflake. 

There are going to be specific clients who need specific data from the overall data lake, those are going to be exposed as APIs. We have multiple customers needing the same data and for this, we move them into the streaming Kafka.

Apigee does not communicate directly with Snowflake. We have data registration, and everything is coming into something that is called the trusted bucket. The  Apigee interface API is written off the S3 bucket. The S3 bucket data is moved into the Delta Lake, and where the data are stored from the Delta Lake, it sends it to Snowflake. We have Apigee going to Delta Lake and S3 bucket, but  Apigee does not go to Snowflake, these are two areas where it goes to. 

We have Kafka consuming directly off Delta Lake, and it sends data to Kafka through the AMQ. We have its setup, and we have interfaces that come directly to Snowflake to pull the data. It is then flattened and enriched, and it is used for many purposes, such as reporting.

Snowflake's most valuable features are data enrichment and flattening.

I have used Snowflake within the last 12 months.

The complexity of the initial setup of Snowflake depends on the use case. However, Snowflake itself, we don't set it up. The difficulty comes from the ingestion patterns, depending on what data I'm putting in, what kind of enrichment, and what additional value we have to add. However, it does tend to get complex because we have a lot of semi-structured data which we need to handle in Snowflake. There have been some challenges.

Snowflake has multiple implementations. For example, it can be implemented on Amazon AWS and on-premise. The data between these two cannot work together because they have different time zones. That's where the integration can be difficult because it is similar to them being on separate islands, they are completely separate. At some point, everything is going to go into the Amazon AWS Snowflake, but right now there are two islands that are completely different. We have to pull the data out and send it out again separately through a different pipeline.

In the future, this type of implementation should be easier. The integration could be better. 

I rate Snowflake an eight out of ten.

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.

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.

The solution is primarily used as a data warehouse.

The solution is very easy to use.

The product is very stable and flexible. The performance is good.

The product is quite scalable.

The solution needs to offer more functionality related to machine learning and artificial intelligence.

The solution needs more connectors.

I've been using the solution for close to two years.

The solution is very stable and extremely reliable. There are no bugs or glitches. It doesn't crash or freeze. The performance is very good.

The scalability of the solution is very good. If a company needs to expand it, it can do so with ease.

We have about 100 people on the product currently.

Technical support overall has been good. They are helpful and responsive. We have no complaints. 

I have some experience with Teradata.

There is no installation process, as it is run as software as a service on the cloud.

For deployment, I would say two to five people would be enough. It depends on the size of the project and can have from one person to 20 people supporting it. It really depends on the implementation. The people would likely be admins, engineers, and managers.

The product requires the purchase of an annual license.

Before choosing this solution, we looked at SQL Server and Teradata.

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.

I use this solution for actively building out the cloud data warehouse and data platform for enterprise level customers as well as startups. Generally, our clients are looking for a data warehouse on the cloud to enable them to scale infinitely at a lower cost. I've worked for a finance analytical team building their data lake, the data platform on top of Snowflake, as well as for a telehealth team. It's basically about getting data from multiple sources and building out an entire data platform with data governance. We are customers of Snowflake. 

One small company I worked with had a MySQL RTS based instance and were using AWS RDS with MySQL on top of that. As a result they were unable to scale their database because there were around half a million queries being run per second as well as data querying and data updating. The migration to Snowflake helped the company because there are no limitations in the cloud and no longer restrictions on the queries. Performance for end users improved whether they were internal or external clients. They used to sell the data through APIs so this migration helped to grow their business overall as well as the ML team efficiency and the productivity of users who previously used the data platform. 

The most valuable feature of Snowflake is the query performance. Snowflake is the most efficient way for real-time dashboards or analytical business intelligence reports to be sent to the customer. There are a couple of areas where they have recently improved. One of the key features they introduced is an internal, table-based merch as well as storing of the unstructured data. You can now build a table out of unstructured data, metadata. This hasn't yet been officially announced.

Although the UI has improved lately, they still need to work on their UiPath, the workspace area.

I've been using this solution for two years. 

It's an infinitely scalable system, but if you use terabytes or petabytes of data, then you need to tune the levels. Each day, we get four to five gigs and overall, our data warehouse has 100 gigs plus, it's huge data. 

Our clients previously used the RTS based MySQL and migrated to Snowflake from there. The primary reasons they moved was because of scalability and performance. Other than that, Snowflake reduces costs quite significantly. I also have experience with BigQuery which is particularly used for Google Cloud although these days they have a multicloud enrollment. Snowflake is vendor independent so you don't have to stick everything in Google Cloud. In terms of performance, Snowflake is faster than BigQuery. 

The advantage of Snowflake is that it's easy to deploy and they take care of the setup. Basically, it's a cloud warehouse and doesn't need to be registered on any website. It's easy. It just requires dedicating space and registering. It shouldn't take more than a couple of days. 

Snowflake is reasonably priced, close to half the cost of some other solutions. 

In terms of performance the solution is good when compared to the analytical workloads and good in comparison to Redshift or BigQuery. The performance is on a slightly higher level, but when it comes to real-time performance, NoSQL is better than Snowflake, but that's in rare cases and depends on the particular requirement. Overall, for the analytical use case, Snowflake is a good solution and in terms of availability, it's a cloud data warehouse, so  they do replication and the like. 

It's important to understand your business needs, because these tools need to be properly modeled and they have their own advantages. If you're new to Snowflake, it's worth starting slowly for one month and move gradually, because if it's a complex system and you move everything to Snowflake without good architecture, then you can get stuck with the original problem. It's worth taking the time to make it efficient and then design modeling; there are SnowPro certifications as well. 

I rate this solution an eight out of 10. 

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.

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.