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.

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.

We have different data models established on Snowflake so our primary use case is to store data from different sources, such as Azure Data Factory, or Databricks. We use it to create the data coming from different sources, and then we store the data. In addition, we also have a reporting structure that we use. We are partners with Snowflake and I'm a lead data engineer. 

The most valuable feature of Snowflake is the Mbps they have established because it's quite a bit faster than any other data warehouse. The solution has different warehouse sizes in place, and we're able to switch through and choose what we want. It can be done in one or two seconds. In other data warehouses, it may take around one minute to set up a different configuration. In Snowflake, it can be done in, two or three seconds. And apart from that, the most important thing given that I have to analyze all this, is that it's very good with the time travel, it's a great feature. 

One thing which I believe Snowflake needs to improve is related to product activation queries. It's not possible to change the warehouse size while executing. It means, for example, that the query is sometimes activated on a larger surface even if I've switched to extra small, the grading in the background is running on extra large. The result is that I have to pay for extra large until the execution is complete. The cost implications can be quite large. There should be a system where they provide a prompt so you can know the current warehouse size.

It would also be helpful if they would simplify the process of using Snowpipe. The way it works now is quite complicated. There are certain steps that you have to follow which is fine but if they could simplify that process, it would be helpful. 

I've been using this solution for a year. 

I've only had one issue related to stability in the past year. 

It's a scalable solution, we have unlimited storage in Snowflake.

The technical support is quite good. We've used them when we've had to implement something and we need some guidance.

The amount of data which we are currently handling, cannot be handled yet in any other data warehouse, but we do have experience with Azure Data Warehouse. We don't have to manage anything with Snowflake, but with Azure we do. Snowflake is much faster, and I believe it is cheaper as well. They have developed it on top of their own system and integrated the concept.

The initial setup is straightforward. It doesn't require any hardware. It's just about getting the account access and having a browser. Everything is done by Snowflake. There is a set process that you need to follow. It is a really fast process, and it helps that the entire migration can be done within minutes. The solution is used on a daily basis by hundreds of people. 

It's pay-as-you-go with Snowflake. The cost is around $US 50 per DB. Terabyte is around $US 50 per month. There are no additional costs. They are divided into two steps to assist the computer power as well as the storage power. Computation cost is relative to the warehouse size. However long our warehouse is up and running, we are charged.

I would definitely recommend this solution. 

I would rate this solution a nine out of 10. 

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. 

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.

The most valuable feature is the clone copy.

In a future release we would like to have a link which would allow us to connect to an external database and create certain views in your own database. This is because it is becoming hard for us to compare the data between multiple sources.

I have used this solution for two years.

This is a stable solution. 

This is a scalable solution. 

The technical support of Snowflake is good. 

Positive

We have seen ROI when using Snowflake based on the insights we get.

I would recommend the SaaS version for their organization. It is not complicated to use. Establishing a private link with current cloud services has been challenging so I would recommend having some kind of a block.

I would rate this solution a nine out of ten. 

We primarily use the solution for the data warehouse.

The solution offers everything you'd find on Azure or AWS. It has a lot of industry-standard features and capabilities.

The product has excellent autoscaling. We can actually compute and scale-out at the same time without having to depend on other tools. You can do it on the fly, or within queries, etc.

The Visual Copy Cloning is definitely one feature that everyone looks forward to due to the fact that it gives you regular backups. 

The solution offers a very good time travel function that allows you to travel back in time to before your systems we corrupted. You can go back into your history and grab the last backup before corruption so that you regain almost everything you need. It gives you 90 days to fetch the data back if you need to. It's better than Azure options.

We've spoken with Snowflake about the fact that there are a few bare minimum requirements now these days for any data cloud, data lake, or platform. They've lacked a bit here, however, they're adopting some new measures that will be available in the next release, so that is sorted.

Snowflake is partners with only AWS as a cloud platform. However, in India, Microsoft has got a big subscription. The product needs to be able to adapt to Azure a bit more in order to meet the local market demands. 

It's difficult to know how to size everything correctly.

They should incorporate at least a basic ETL framework.

It's early days, however, I would put the solution at a seven out of ten. It needs a bit more time to mature. If I were to look at it strictly from a warehousing perspective, I'd rate it at an eight out of ten.

I've been using the solution for about ten months. I started using it originally when we started our partnership with the organization.

The one thing that seems to be unclear for Snowflake customers is the cluster sizing. No one seems ot know how to compute that.

For example, if I'm running a warehouse that is extra small, as per my query performance, if I see like if this query will run perfectly on the machine I will have. However, I don't know which machine to go for. There's no direct comparison between an extra small, or a small, or a medium warehouse. I never get to know, unless I run the case query on different sizes, which to go for. It's hard to say "Buy only this and go for that particular size". Sizing seems to be a bit of trial and error. If they had some sort of benchmarking around their cluster size, that would be helpful.

The initial setup is pretty straightforward. We didn't have any issues with implementation. It's not too complex.

The pricing of the solution is fine. The storage is pretty cheap. They also offer a lot of discounts. The cost shouldn't really be a problem. 

That said, the agreement should be more of a subscription basis instead of asking for a commitment. For example, Microsoft tells your the price and allows you to subscribe to that, whereas, Snowflake wants you to commit to a certain amount of time before they really give you firm pricing. 

We're partners with Snowflake. We've been partners for just under a year at this point.

I'd definitely recommend the product. It's worked quite well for us. 

A new customer needs to understand, however, that they need a roadmap of at least five years when they are deciding on their data warehouse. They should compare costs and sizing to make sure they are getting the solution that makes sense for their current and future needs. 

The solution integrates well with other applications, and if you need it to integrate with existing applications, you still should check to make sure it's possible.

I wouldn't necessarily recommend Azure over Snowflake, as they aren't really a good comparison. Snowflake is more focused on data repositories and data warehouses. AWS does give you many options, however.

We are a big data company. We have many thousands of devices deployed from our customer base. These devices upload data, on an hourly basis, to a central storage. Next, we run some ETF processes that crunch and process data, then we store that data in a structured way on Snowflake.

Over the past six months, it has been more of a development project.

I am using the latest version.

• I like the idea that you can assign roles and responsibilities, limiting access to data. That has been very interesting to us. 
• The robustness of the system is very useful to us. 
• The availability of the system. 

We would like Snowflake to be able to do inter-cloud migrations. That would be great. I want to be able to switch clouds.

Six months.

It is stable.

If you go with one cloud provider, you can't switch.

We have very few users. There is just a very limited number who are mostly developers. We did not roll out the end product. We did not roll out product and services based on that foundation/infrastructure yet.

I have not personally contacted technical support.

We previously used HPE and Microsoft Insight. We switched to Snowflake for the availability, security, and loading times.

The initial setup was very straightforward. It was very quick. The complexity came from our specific use case scenario.

We also looked at HPE and Microsoft Insight.

Analyze your user scenario. If your scenario is managing large amounts of data in extremely, different environments in a structured way, then this is a good option.

I would rate this solution as an eight (out of 10).