Snowflake Reviews

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 this product basically for developing an IoT (Internet of Things). Currently, we are sending data from our S3 (Simple Storage Service) storage. In the future, we are planning to directly stream data to Snowflake.

In terms of features, we have not yet explored the product completely. So we are still in the proof of concept phase. As of now, the product meets our preliminary requirements. The snapshot feature is good and the rollback feature is good. That is why we have the product and these are the key things we need.

The recovery process is very simple and very user-friendly as well. The constraint files could be improved. But the most important things are there. Also, the interface is good to work with and user-friendly.

If I had to say what needs improvement, in my experience is that the availability is a problem. Availability should be high and has to be enhanced.

I still have not explored all of the existing features. Because I have not been deeply involved with using the program it is hard to say what else is missing or what I would need.

We have just explored the product for a few months.

The stability of Snowflake is very good.

Scalability is very good and very simple.

Within the company, we currently have a total of fewer than 50 people using the product. Their general roles vary from data analyst to data scientist, to IT professionals, and maybe one or two of the upper managers.

I tried using technical support once and they were very helpful. But after that, I have not had any reason to explore the support services.

The initial setup was simple.

The implementation was done by the vendor.

Before choosing to work with Snowflake we looked at virtual solutions and we looked at Redshift, we looked at Qubole and a couple of other cloud options. Earlier when we did a mining site, I was working with Alteryx, so I was already familiar with that product and its capabilities to use for comparison. We decided to go with Snowflake because it seemed mature and had what we wanted.

My advice to someone who is looking at Snowflake is that if they are looking at analytics tools in addition to warehousing and certain advantages of cloud computing, then I would suggest Snowflake. But if they are just looking for the warehousing part where you will later just use another solution on top of it, then I would not suggest Snowflake.

On a scale from one to ten where one is the worst and ten is the best, I would rate Snowflake as an eight or nine. Let's say 8.5.

The solution has use cases related to retail stores and sales.

The most valuable features of Snowflake are that you have to pay per usage, and you don't have to worry about the maintenance of the data warehouse because it is on the cloud.

The solution’s pricing could be cheaper. It would be helpful if Snowflake could create good reports instead of using Power BI reports.

I rate the solution a nine out of ten for stability.

Snowflake is a scalable solution. We have four to five customers for Snowflake who use it regularly.

The solution’s initial setup is straightforward.

The solution's deployment in a development environment takes only a couple of minutes.

Users have to pay a licensing fee for the solution, which is expensive.

Snowflake is deployed on the cloud. The solution is providing HIPAA compliance, which is sufficient. Users looking for a pay-as-you-use product available on Azure or AWS should consider Snowflake.

Overall, I rate the solution an eight out of ten.

We have a data mart, and we are using it to share data with big enterprise customers with major security requirements.

Snowflake is an enormously useful platform. The Snowpipe feature is valuable because it allows us to load terabytes and petabytes of data into the data mart at a very low cost. Then we just share it out, and all the compute expenses are charged directly to our clients.

It would be better if they had a data profile tool that tells me where the gaps are in my time series data. We are anxiously waiting for them to release their data catalog and analytics capabilities, which is going to happen in June or July. If that works the way we think it might, then that would just extend our firm's capabilities into a space that we have never been interested in building ourselves. It could be a really good thing for us.

We started using Snowflake this year.

There's never any outage, and it's cross-cloud. The stability is not even a good question for that platform. It makes no sense to us.

Snowflake is scalable. It does cost more money, but it's some kind of magic they're doing behind the scenes that you don't have to think about. It's brilliant, and it's going to take over completely.

Their tech support is good. Their sales team is very technical, and they're able to speak to our engineers and walk them through what we need to do. 

About three years ago, Databricks was sort of the hot thing among our clients, and everyone was using it for low-code analytics. We had to deliver data in a format that was specific to Databricks. Databricks had this massive growth, use, and adoption. They have a very good footprint now, but we see those same clients shifting their data to Snowflake, and pretty much nobody asks for Databricks anymore.

I think there's this big war sort of brewing between Databricks and snowflake. Snowflake is going to come out with the analytics capability that Databricks has. They're working furiously to get it released. I don't know what it's going to look like, but they're going head-to-head with Databricks. I think Snowflake is going to crush them.

In the beginning, we didn't know what we were doing, and we racked up huge compute costs, shockingly and quickly. But the sales team was extremely helpful and showed us where we were doing everything wrong, and they explained to us how best to use their platform. We have massively funded data engineering teams, but now our use has plummeted to almost free.

Because of the caliber of our customers at the time, we had to sign on to the enterprise subscription tier. We're a startup, and we didn't know it at the time, but the cost per credit for the enterprise tier was almost double. 

The cost per credit, that's where you get all this unlimited autoscale that you don't even have to think about. We don't really need any of that because they already provide all the redundancy, backup, failover, and all of that stuff. We scaled down and cut all of our costs almost in half by getting rid of that scalability capability because we don't need that.

They give a different price for every single company. I don't know if I negotiated that well, but we got the enterprise tier for $3 a credit, and the other two were a dollar-ninety a credit. I suspect we don't have almost zero compute usage, but I know that our annual contract packages are below all of their minimums.

On a scale from one to ten, I would give Snowflake an eight.

The platform's most valuable features include its ability to effectively summarize and manage large datasets, allowing multiple teams to analyze and generate insights. Its integration with data lakes for business impact analysis, performance metrics, and KPIs is particularly important.

Improvement is needed in integrating external tools, such as data catalogs, which can be complicated due to differing formats and usage across departments. The goal is to enhance collaboration and streamline workflows.

The product's scalability is crucial for managing petabyte-scale data generated daily across various regions, allowing for efficient data validation and handling.

The primary challenges during the initial setup were the high pricing and uncertainties regarding future costs associated with data usage. 

The deployment involved consultation among managers, agreement on on-site requirements, scale calculations, and collaboration with engineers for setup approval.

I rate the process a seven out of ten. 

Snowflake is integrated through a complex workflow that involves collecting data on the publisher side, using tools like Airflow and Kafka for batch jobs, and frequently importing data into the product from various sources, including S3 and Data Lakes. It creates a smooth data pipeline.

I rate it a seven out of ten. 

I am using Snowflake for all our apps and data warehousing requirements.

The most valuable feature of Snowflake is it's an all-in-one data warehousing solution. 

Snowflake could improve migration. It should be made easier. It would be beneficial if it could offer some OLTP features. One of our customers was using Oracle for both data warehousing and OLTP workloads, and they were able to migrate their data warehousing workloads to Snowflake without major issues. However, for some of their OLTP requirements, such as needing a response time of fewer than 10 milliseconds for certain queries, Snowflake is currently unable to provide that.

It would be beneficial to see more integration and reporting tools embedded within the platform, similar to what Microsoft offers with its data warehouse and database solutions. Oracle, on the other hand, does not have such features. While Snowflake has a lot of options available on its marketplace, it would be helpful if it could provide more optimal options for users who are migrating from other environments. It would be great if they could follow the same path as Microsoft in this regard.

I have been using Snowflake for approximately one year.

Snowflake is stable.

We have two clients using this solution.

The solution is highly scalable.

We don't have direct support for Snowflake as we support our clients. If any support is required, our clients connect with Snowflake's support team and get the necessary assistance. They own the support account.

The initial setup of Snowflake is easy. We only need to create a login, there is no conventional setup.

When it comes to migrating from a previous platform to Snowflake, it largely depends on the size of the data warehouse, the number of integrations, the existing data pipelines, and the type of data sources. The complexity and size of the current deployment will determine how difficult the migration process will be.

The ROI appears good on paper, particularly in terms of cost reduction in operations. However, as companies have only been using Snowflake for a year and it's hard to say for sure. It looks promising for now, but it will take a year or so to see if it holds up in practice.

Snowflake is a cloud-based solution that uses a pay-as-you-go model. The storage and processing are separated, and you are mainly charged for the processing power you use. Additionally, there is a charge for storage, but the primary cost comes from the processing. You can choose different sizes of processing units, also known as warehouses, for your specific workload or requirements. You pay for the per-second utilization of those computing resources.

Snowflake is cost-effective. However, the cost can depend on how it's being used and how efficiently the code is written. If engineers don't write efficient code and usage is billed based on processing, it can become costly. If they write optimal code and choose the best solution, it can reduce costs in comparison to other options, such as Oracle.

I would recommend this solution to others.

I rate Snowflake an eight out of ten.

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.

Snowflake is a real-time and cloud-based complete ETL tool. You can receive the beta from various sources from Amazon. You can run your reports and do analysis in  Snowflake. Informatica and Tableau should have done this. Snowflake is a modern version of Informatica which is 100 percent in the cloud.

Snowflake has a variety of other ETL provisions that they provide. You can use your own ETL pipeline. Additionally, they provide adapters, and they are always evolving, it is a well-developed solution.

Snowflake has to build more capabilities because they have only built very few adapters, but they're growing and they're building. They should provide provisions to collect ETL pipeline capabilities, reduce developer work, and make more rapid application development, rather than some customizations. There are very few options, but they are building. I hope they will build ETL rapid application development provisions with more variety.

I have been using Snowflake for approximately eight months.

Snowflake is highly stable.

Snowflake is a cloud solution that provides great scalability. However, I am not sure if it is cost-effective.

We have approximately 30 engineers using this solution. We have plans to scale our usage in the future. This is going to be a futuristic solution.

We have not had any problems with the technical support.

The initial setup of Snowflake is straightforward. To set up the ETL pipeline, pull the data, and then generate the reports takes approximately two hours, end to end.

I did the implementation in-house. We have a three-member team that does the maintenance of Snowflake. However, the amount of people needed depends on the size of the pipeline.

Snowflake licensing is more flexible and it is cheaper than other solutions. I can use it for only 10 days for MVP, or three years, and for flexible models. I can scale up, or down, and the pricing is based on the volume and duration. There are many licensing permutation combinations available.

I rate Snowflake a nine out of ten.