Snowflake Reviews

We're using it more for data warehousing and distribution.

Snowflake is a SaaS platform, so I'm using whatever is the latest version.

It's definitely for compute. The best use case of Snowflake is massive compute. With the parallel reads that we can do from Snowflake, we can combine data from disparate sources, consolidate it, and provide it to end clients through custom stored procedures.

It's user-friendly. It's SQL-driven. The fact that business can also go to this application and query because they know SQL is the biggest factor. So, we can provide all the data, and the analysts, data scientists, and product strategists can go and analyze the data themselves.

Room for improvement would be writebacks. It doesn't support extensively writing back to the database, and it doesn't support web applications effectively. Ultimately, it's a database call, so if we are building web applications using Snowflake, it isn't that effective because there is some turnaround time from the database.

I'd like them to look into the limitations of REST API. Snowflake came up with this native API concept, but it has got a lot of limitations. I'd like to see it provide better service-based APIs so that it can provide data as a service.

I've used Snowflake for over three years.

Its stability is fine, but of late, I get loads of messages saying there's some sort of outage or some sort of issue in the application. I keep getting these notifications from Snowflake, which gives a false impression that something wrong is happening, and it might be underlying in the backend. It doesn't seem that stable.

Its scalability is high. I'd rate it an eight out of ten in terms of scalability.

At this time, we have no plans to increase its usage.

Their support is good.

Prior to Snowflake, it was a completely Greenfield requirement.

It was very straightforward.

It required just two people. One from the Snowflake perspective, and one from my team members' perspective to get the configuration running. That's it.

We haven't yet seen a return on investment because some of the applications are yet to be fruitful and make revenue. We have used Snowflake for the past three years at this point, but we have not yet made great revenue.

It's expensive.

Snowflake is very useful as a data lake and as a data warehouse. Also, it has a lot of features with respect to data science. We are not there yet, but if there are any specific use cases around compute, data distribution, and data sharing, then Snowflake is a tool to be considered.

I'd rate Snowflake a seven out of ten.

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.

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.

Our primary use case for Snowflake is inputting data generated by AWS.

This solution has helped our organization by being easy to maintain and having good technical support.

The features I have found most valuable are the options to connect with extendable sources in three buckets in which we can also create stages.

I think that Snowflake could improve its user interface. The current one is not interactive.

I have been using this solution for about one year.

I would rate the stability of this solution a nine, on a scale from one to 10, with one being the worst and 10 being the best.

I would rate the scalability of this solution a 10, on a scale from one to 10, with one being the worst and 10 being the best. There are around five developers in our company and 500 end users for this solution.

We previously worked in AWS.

I would rate the initial setup process an eight, on a scale from one to 10, with one being the worst and 10 being the best.

I would advise other people trying to use this solution to build a skill balance as it's quite difficult to work in Snowflake.

I would rate this solution as a whole a nine, on a scale from one to 10, with one being the worst and 10 being the best.

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.

Snowflake is good for analytical purposes when you have a lot of historical or sales data that you need to release and use for different types of analysis, such as tracking sales and measuring the performance of your sales team and product.

The product's most important feature is unloading data to S3. It provides a single syntax query to analyze data directly from a database to an S3 bucket.

The product's performance could be improved.

I have been using Snowflake for a couple of months. We are using the latest version.

The product is stable.

We did not need to scale our Snowflake environment beyond what we needed. We have a fixed amount of traffic from a fixed number of clients. We know the load we need to handle, and based on that, our subscription is made.

Snowflake is cost-effective. The pricing is better than Firebolt. Firebolt is better when there is idle time. If we run Snowflake all the time, the cost will be higher.

We are working on two solutions for Snowflake, one for the cloud and one for on-premises. It has good documentation. If someone goes through it, they will quickly understand how it works. However, Firebolt's documentation is more comprehensive. If I need faster results, I'll prefer the Firebolt; if I need performance, I'll use Snowflake.

Overall, I rate it a nine out of ten.

We use the solution to build the pipelines in stream sets, including data source, data warehouse, and destination endpoints.

The solution's most valuable features are storage, run time, scalability, and minimum query time compared to other vendors.

The solution's stability needs improvement.

I have been using the solution for seven or eight months.

Recently, I encountered an issue with the solution's data warehouse. The resource monitor had exceeded its quota. I rate its stability as an eight.

I rate the solution's scalability as a nine.

We use Hive and Hadoop as well. Snowflake is more stable and scalable.

The solution is more straightforward to use than the other IDBMS tools. It has a simple query process. Its computing time is less as well. One can easily have access to it. I rate it as a nine.

Snowflake is used for very large data, such as in the case where tables might contain 600 to 700 million records.

It's ultra-fast at handling queries, which is what we find very convenient.

The pricing and licensing model is good.

Snowflake has support for stored procedures, but it is not that powerful. They have a lot of limitations. For example, it is really basic and there are limitations on subqueries.

The functions are not very good. Improving this would help to make sure data manipulation much easier. Right now, the inbuilt stored procedures and functions are all Java-based.

I Have been using SnowFlake for about five months.

We have approximately 10 people in the organization who are using Snowflake.

The technical support is very good.

We use Snowflake in conjunction with Matillion, which is another AWS-based ETL tool. It is being used as a bridge between our on-premises data and Snowflake. 

The initial setup is very straightforward. You simply log in and start using it.

When it comes to deployment, you can choose between the AWS and Azure cloud. We chose AWS.

It is easy to create an instance and you can do it yourself if you have an AWS account. Snowflake will give you the connection ID and other relevant details.

The pricing is flexible in that, for example, if I run a query and it is slow then I can increase the processing power while it is still running, and they charge more for the time. The cost is on a per-query basis.

If you're running with a base processor, called a warehouse, the query might cost 1.0 cents. But, if my query is slow and I want to increase the speed, the next level adds a little more cost to that.

On average, with the number of queries that we run, we pay approximately $200 USD per month.

Recently, we have been doing a review of Redshift. However, we finally decided to go with Snowflake.

My advice for anybody who is considering Snowflake is that it is a really good product, especially if you are having issues with Big Data. It is not good for a typical OLTP environment, such as a small table.

I would rate this solution an eight out of ten.