Snowflake Reviews

The primary use case is for building a database and data link.

I like the ability to work with a managed service on the cloud and that is easy to start with.

From the documentation, the black box is not very descriptive. Snowflake does not reveal how exactly the data is processed or sourced.

I have been using Snowflake for three years now.

The stability is reliable and a standard product.

The scalability is very good and we have around two hundred data sets currently operating.

Technical support is good. It is readily available and they are very responsive.

Positive

The initial setup was straightforward.

You can do the implementation in-house since it is a managed service and only takes a few hours.

The pricing is economical as compared to traditional solutions like Oracle and competitive pricing.

I would rate Snowflake a 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.

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.

The primary use case is data platforms, specifically data warehousing. It involves restoring and moving data within the platform to prepare it for analysis, routing activities, or serving as the backbone for applications. 

Snowflake also advertises different workstreams, but my customers mostly use it as their core platform to ingest data and serve the onward goals of the wider company.

The most valuable feature of Snowflake is consumption-based costs, which means that you only pay for the storage and compute you use. There's a complete separation of storage and computing, so you don't need to add another server to increase storage or computing. From a costing perspective, it's well-positioned. 

Snowflake's time travel is also incredibly useful, and they have a function called "UNDROP," where you can undo a table drop. Data sharing and replication for Snowflake are strong, and they have a data marketplace with public and private data sets available for sharing. Companies can put their data on the marketplace, and anyone can use it by starting the payment model. The data is provided live straight to you, and it appears as if it were just another database in your own environment.

The main thing I'm excited to see at some point with Snowflake, hopefully - I've not seen anything coming out of it yet - is Git integration into the worksheets and the UI. Sometimes it can be tricky to manage multiple environments if you're purely using Snowflake as your scripting and pipeline environment. This is handleable, so if you use third-party tools like DBT, Matillion, etc., those can help. But if you're looking purely within Snowflake itself, it'd be great to have some form of Git support.

For the future releases, I would love it if they one day decided to implement their own GUI-based transformation tool environment. I know that many competitors like Azure have to Sign Up, and Azure Data Factory can sit in. However, Azure is a very different beast that serves all sorts of different processes, and an argument could be made for whether it's the best to each of those or not. Specifically within Snowflake, I would love it if they could get some form of orchestration built-in for transformation that doesn't have to be controlled directly through code all the time.

I have been using Snowflake for five years. 

It is an incredibly stable solution. It will only go down if your cloud provider itself goes down. So, let's say your Snowflake is hosted in Azure London. If the Azure London data center goes down, I would only see Snowflake going down. If that does happen, Snowflake does have plenty of options for failback replication and rollover backups. 

So we have quite a few customers that, for example, need their data restored in AWS London, and they've got a backup or a replication stored in Azure London. If AWS London goes down, then Azure London one will kick in and become the primary account, and all of the URLs, etcetera, remain the same because they've set up failover URLs and connections for it. At least for the end customer, there's no change. It's only for the architecture and developers behind the scene who then have to double-check things and do all the normal due diligence. But it runs very smoothly

It is a highly scalable solution. There is no limit on storage or computing. They have everything on consumption-based pricing, but you can have what's known as a multi-cluster warehouse. So, warehouses are what you use for the compute.

The multi-cluster warehouses will sit there originally as a single cluster. But then, if there are enough concurrent queries taking place in that warehouse, it can, as it needs, just spin up another one from another one and another one to meet those current needs. And as soon as they can dive down again, it can switch those clusters off again one by one. And you can create as many clusters, warehouses, as many as you need. There is no scaling issue at all. I've seen it most, like, 10,000 queries a second, and it's run very, very smoothly.

The customer service and support team is very useful and strong. They've got support built directly into the Snowflake UI. So wherever you are on the platform, and you see an issue, you can click into the support area and submit your ticket, including direct things like the query ID that you're using or multiple query IDs and all that stuff. 

I find Snowflake to be very responsive, and if you submit a top-level ticket, you can get a response very quickly. The lowest tier of tickets might take 48 hours sometimes, but overall, they are very helpful.

I personally don't see any of the competing cloud platforms coming close right now to what Snowflake offers. An argument could be made with GCP and Datadog are getting closer. Also, a new AWS Redshift is on the horizon, like a whole new AWS Redshift 2.0. But right now, I've not seen anything that comes close. Snowflake, to my understanding, is the only platform that fully separates your storage and computing, essentially. And it's the only platform I've seen with things like time travel. It's got a whole bunch of great features that I don't know if other tools also have, but it supports semi-structured data. It supports automated tasks, alerts, and reporting. And the data sharing is a massive one. GCP now also has its own data-sharing potential, where you can share data with other GCP accounts. I've not used it myself, but to my knowledge, whilst they have the sharing, they don't have anything that even comes close to the Snowflake data marketplace that allows customers to sell or share their data outside the wider world. And it doesn't have anything that comes close to the kind of private equipment where customers might share their own data internally or to their own. And I think there was one more thing. 

Snowflake also have some really good support for Python, Scalar, and Java through what they call Snowpark, which was launched last year. But more recently, this year, it was announced they're really pushing forward with their StreamLINK integration. It will allow customers to host applications on Snowflake and share those applications with other users in a very similar kind of marketplace environment they use for data sharing. I don't think there's anything that any of the other competitors have right now.

The deployment model is delivered as a service. So the most deployment you have to do yourself is by deciding which cloud provider and region you want it to be hosted in. But Snowflake will actually host it themselves, so there's no deployment beyond clicking from a dropdown and clicking okay, and it'll magically appear.

Moreover, it's very easy to maintain because it's delivered entirely as a service. Snowflake takes care of all the patches, upgrades, maintenance, security tweaks, etc.

We have many long-term customers who have been using Snowflake for years, and they wouldn't continue to use it if they weren't seeing a strong return on investment.

There are many options for starting a Snowflake deployment, but I recommend working with a partner who can provide best practices and guidance. It could be through Snowflake directly or another service partner. Working with a partner can save you time and prevent mistakes down the road.

Overall, I would rate the solution a ten out of ten. 

I am using Snowflake for migrating data and table backups.

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

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

I have been using Snowflake for approximately three years.

The stability of Snowflake is good.

Snowflake is a scalable solution.

We have approximately 200 to 300 people using the solution.

The initial setup of Snowflake is easy.

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

I rate Snowflake an eight out of ten.

We use Snowflake for our data warehouse. Amazing product. Redshift cannot compete with a true elastic data warehouse where you can scale computing by just issuing a SQL query (increase computer power) and resizing it down or putting computing unit to sleep. 

Snowflake has many more features:

When combined with Alooma, it's the best data integration system. No need for Talend and all these cumbersome tools.

We were able to implement the entire data eco-system in less than five months, from data integration, data warehousing, ELT, producing fact and dimensional tables, and finally reports.

• Computing unit is accessible via SQL: being able to turn them on or off as needed.
• Snowpipe (ingesting data)
• Looking back in time (being able to look at data in the past within a query)
• No data warehouse management
• Support for JSON.

The list is pretty long.

• Snowpipe auto-ingest should be automatic.
• A better client UI or command line tool: I think SnowSQL is a little awkward.

Excellent.

Excellent.

Used it at a previous company.

Yes. No hardware or server config is needed. Just create a user account.

In-house.

Very good.

Snowflake computing is very affordable. Less expensive than Redshift.

Yes. I looked at Redshift and Vertica.

I mostly build or design data warehouse analytics solutions using Snowflake.

The productivity across the teams that use Snowflake I believe is higher than it would be without the solution.

Snowflake has three great features: Snowpiping is proving to be very valuable, Time Travel is excellent, and Snowpipes are another great functionality the solution has made available.

Snowflake needs transparency over costs and pricing.

I have been using the solution for three years.

I give the stability an eight out of ten. We rarely have issues.

I give the scalability an eight out of ten.

I have used many on-premise solutions in the past and also Azure Synapse Analytics.

The initial setup is straightforward. I give the setup an eight out of ten.

A basic implementation is quick and we only have to set up Snowflake and the cloud service. We need one engineer and one designer for the implementation.

Depending on the complexity of the implementation, we may need to use a third party or help from the vendor.

Part of the problem with the pricing is that it is very difficult for businesses to get an idea of how expensive it might be until they actually start using Snowflake. Until they start pushing large amounts of data through, we will not really know. I believe that it is very difficult for businesses to make a commitment.

I give the solution an eight out of ten.

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

We're using Snowflake for Power BI Cloud. We had a cloud version of Snowflake, so we were connecting to the Snowflake database and importing required tables into our system, Power BI Desktop. From there, we linked those tables and created a semantic layer, an internal layer between the frontend and backend, and then we tuned the data. Then we used both the tables to tie into the dashboards that we developed. The dashboards show the sales information or marketing information.

It's a cloud solution.

I like the entire database. This is the advanced version of the cloud version, so it's really a flexible tool. If you have it implemented at home, you can access it from anywhere.

Sometimes when I'm trying to refresh the data, my different application or tool has to connect to its backend database through the connection I create. Sometimes, I face some issues like not having permissions. These aren't as crucial, but there are common errors sometimes where the database is down, or a table is nullified and a new table is added and you are not given access to that. With those errors, you don't have permissions.

I have been using Snowflake for a year.

It's stable.

The scalability is good. It handles a lot of data, and the processing speed is very high.

It's straightforward. You have to have a rule, database names, and a schema name.

When somebody deploys it and gives me the URL and the required tables to use, I use the URL and configure it from the frontend side, reporting side that could be more like Power BI or Tableau, and I start using it.

I would rate this solution 8 out of 10.