Snowflake Reviews

We're ingesting third-party data analytics into a database held within Snowflake. We have pre-production and production environments with integration to staging and production schemas.

It has improved the way our organization functions. However, we're still pretty elementary in our understanding of how it all works and the complete capabilities of Snowflake.

The adaptation to development languages is most valuable. Our developers can SQL code or something else. It has been convenient in that regard.

The data sharing capabilities across business units within the organization should be better. There could also be better integration.

I have been using Snowflake for a year and three months.

We have high confidence in it.

We have high confidence in its scalability. In terms of its users, for our solution, we only have a team of 10, but we have plans to increase its usage.

We haven't had any technical contact. All of it has been internal for our organization.

This is a net-new solution. So, it's brand new. We chose Snowflake for a variety of reasons, but mainly, we chose it for its scalability and data sharing capabilities.

I would rate it a three out of five in terms of complexity just because we didn't have any Snowflake developers that were available. The implementation took about three months.

We implemented it on our own.

We have not yet seen an ROI.

We're based on credits. So, we're paying four and a half dollars of credit. There are no additional costs. I would rate it a two out of five in terms of pricing.

I would advise ensuring that you have the expertise with domain knowledge in Snowflake. The time from initial concept to deployment could be expedited extremely fast. Just from our internal learnings, we see that our time to production has increased month over month.

I would rate it a six out of ten just because we're unaware or naive to the full capabilities of the product. However, I would highly recommend it in terms of setting up data warehousing internally over an Azure solution, such as Synapse, or something else.

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.

We implement this solution for our customers. It is a cloud data warehouse. It is SaaS, and it can be run on Azure, AWS, or something else. We are using its latest version.

It is a very good platform. It can handle structured and semi-structured data, and it can be used for your data warehouse or data lake. It can load and deal with any data that you have. It can extract data from an on-premises database or a website and make it available in the cloud.

It has very fast implementation and integration as compared to other solutions. There is no need for the DBA to manage or do the day-to-day DBA tasks, which is one of the greatest things about it.

In future releases, it can also support full unstructured data.

We have been using this solution for a year.

It is stable.

It has very good scalability. Your data can grow in the platform. We have at least 50 users of this solution in an organization.

Their vendor is wonderful. I only have good words for them.

It is not too complex. Its implementation is easy even for those people who don't know Snowflake and are coming from other environments, such as Oracle or SQL Server.

It can be implemented very quickly. Our customers in Israel implemented it very quickly. It was much faster to implement than other platforms.

It is on a monthly basis. It is based on your usage. There are no additional costs from the point of the licensing fee.

We do give some kind of evaluation to the customers about how much it is going to be. You can decide in Snowflake the virtual machine that you are using for customers. There are several kinds of virtual machines that you can use. It is similar to the clothing sizes: small to extra large. If you need more power in the coming month, you can decide in advance and take a more powerful machine. You can just select it from the platform. You can also decide which machine you want to take for extracting data.

I would advise others to check themselves how fast its implementation can be and how responsive it is. I would also recommend evaluating it before choosing other solutions, such as Microsoft Synapse or Amazon Redshift. You can test it yourself by using a test case. You can try to load the data on each platform, which can take a few weeks, but you will get to know the advantages of this solution. It is very different from other solutions.

I would rate Snowflake a nine out of ten.

Our company uses the solution for building a data platform, data warehouse, and data transformation.

The product is somewhat used for data analytics, but it is mostly for data engineering.

The tool is good for handling large datasets, and since the tool is fully managed by Snowflake, you can scale up the compute part.

I don't think that the AI tools in Snowflake are good. AI tools in Snowflake can be improved. Even if the AI tools in Snowflake are good, I feel that it would be expensive. The cost of the AI part does not justify what you get from the product.

The price of the product can be lowered.

I think Snowflake should integrate with some tools like ChatGPT.

I have been using Snowflake for a year.

The product is scalable and can be considered a good fit for small and medium businesses.

I haven't directly contacted the technical support team of the product.

I have used Azure Databricks and Azure Data Factory. My company decided to use Snowflake since we wanted to be able to get up and running fast without much configuration-related mess. Snowflake doesn't give you the options with the configuration part since, by default, it is available out of the box. In terms of machine learning, Azure Databricks has the upper hand over other products.

The product's deployment phase was quite okay.

The solution can be deployed in a few days or up to a week.

The product's price range falls between average to a bit expensive range. I think the tool is worth the money if you use it properly. It is difficult for me to speak about the number of users who use the product. My company pays around a couple of thousand dollars a month to 10,000 dollars or more.

I think the main benefit is that with the tool, you can easily get things going without problems since you don't need to configure all the parameters manually. If you buy the tool for a bigger computing purpose, the engineer can pay more attention to the tool, and I guess after that, you can do more with the solution. I would ask others not to think about the data warehouses, as Snowflake takes care of such areas.

The benefits from the use of the product can be realized in around 40 minutes. It is a good technology for getting up and running quickly.

Snowflake is integrated with Azure Data Platform and other ETL tools in our company's ecosystem.

The integration capabilities of the product are good and you get what you pay for when it comes to Snowflake.

I rate the tool a seven to eight out of ten.

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. 

We use this solution to ingest e-commerce advertising and web analytics data and completing an analysis. There are 60 people using this solution in our business. 

We are working with a TV advertising agency and they were able to set up a Snowflake data share to share ad spend with us and it was very quick to integrate.

The most valuable feature has been the Snowflake data sharing and dynamic data masking.

The cost efficiency and monitoring of this solution could be improved. It's easy to spend a lot on Snowflake and it does offer monitoring tools but they're pretty basic.

I have been using this solution for three years. 

This is a stable solution. 

This is a scalable solution. 

I would rate the technical support for this solution a four out of five. 

We previously used Amazon Redshift.

 The initial setup was straightforward.

We did have a consultant help us.

We do see a return on investment.

Pricing is based on usage. It is the most expensive of our data tools.

I would advise others to check costs when implementing any changes, such as new BI tools or a new data source. Set up different warehouses for your different tools so that you can track cost.

I would rate this solution a nine 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.

Data warehousing is typically a rich guys' toy. Large enterprises are only able to leverage data warehouses for data analytics purposes. We wanted to change that and wanted to build a data warehouse template model for businesses across industries. 

If Snowflake was not around, we would have used Google's Big Query or Amazon's Redshift, or a MYSQL/Postgres database in a Windows VM (virtual machine). However, Snowflake made it a lot easier for us with loads of features such as encryption of data in motion and at rest, masking policies, time travel (to correct data load issues), controlled access based on roles, data sharing, third-party data from marketplaces, etc.

It helped us to build MVP (minimum viable product) for our idea of building a data warehouse model for small businesses. 

About ten years ago, force.com from salesforce.com offered a similar platform for us to build data warehouses. However, our staff with a data engineering background found it easier to build the data warehouse in Snowflake, with the easy-to-use SQL interface and RBAC models (role-based access control). The platform saved us money as it automatically shuts down the compute engines after about five minutes of idle time. Per second billing (above the first minute) is great. 

In my view, cost optimization for the computing power required by the ETL jobs, reports, and dashboards is the most valuable feature. Especially for startups, this helps us to keep cost spending within control without having to worry about manually shutting down the server when not used.  

As a Google partner, we like to leverage GCP (Google Cloud Platform). Snowflake supports GCP, AWS & Azure platforms. This works just fine for us. Encryption of data with multiple keys for both data in transit and data at rest gives us enough confidence to use snowflake for our customer 360 solutions.

Currently, we use Snowsight only to monitor the usage of the Snowflake environment by our users. However, if Snowsight can be improved, we can host our BI (business intelligence) environment also within Snowflake. In our case, to provide basic reports and dashboards, we started to use Tableau, Power BI, Looker, and Qliksense, depending on our customer preference.    

We are yet to figure out how to integrate tools, such as Liquibase, to release changes to our data warehouse model. If Snowflake could guide us with some easy-to-use integration (similar to DBT integration), that would be great.

I've used the solution since 2020.

Stable

Scalable

Support can be enabled in the Snowflake UI.

Neutral

In the past, we used Google Cloud SQL. However, Snowflake offered cost optimization among the many other useful features. They also introduced app building on top of the data hosted. 

The initial setup is not difficult. Google Search will lead us to articles that can guide us on the setup of users, roles, warehouses, and access controls.

We did the initial setup on our own, and it was not difficult.

We constantly monitor the usage with grafana dashboards to keep the ROI growing and to assist/ alert users about any wastage.

Many interesting features are available only in the enterprise edition. Check out the differences when you are evaluating the product: https://docs.snowflake.com/en/...

We considered MySQL and Google Big Query. We're also happy with Google Big Query. 

Snowflake is growing with newer features and capabilities. But not much success with Stream lit app. Big query + app sheet is an alternative that we're considering.