Snowflake Reviews

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.

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.

My company wanted to have all our data in one single place and this what we use Snowflake for. Snowflake also allows us to build connectors to different data sources. The ultimate goal is to provide reporting and analytics to all departments in the company.

This solution could be improved by offering machine learning apps. 

We have used this solution for one year. 

This is a stable solution. 

This is a scalable solution. 

Before Snowflake, we used Azure.

I worked with a Microsoft partner to set up the entire thing. This took three months. 

We have not yet experienced ROI. 

Overall, this is a helpful tool with a friendly interface. 

I would rate this solution an eight out of ten.

My current client is migrating from an on-premise data warehouse to the cloud, and they need to consolidate the data lake. I'm using Snowflake to develop the data lake and build data bots for certain functionalities. I've also used Snowflake to aggregate and clean data for my client's analytics.

I like Snowflake's data exchange capabilities. It can exchange data with downstream systems and other vendor partners as well.

There are some challenges with loading unstructured data and integrating some message queues or brokers. In one project, we had a problem connecting to one of the message queues and we had to take a different route altogether on Microsoft Azure. 

I started using this product about two years ago at my last company. Now, I'm a registered Snowflake partner, so working with Snowflake for one of my clients.

My clients haven't faced many stability challenges. It is a relatively new product that's still evolving. They're using it mainly for POCs, so they haven't faced any problems. 

The most valuable aspect of Snowflake is its scalability. The volume of data, which I have seen across almost in terms of five terabytes and other terabytes of data, I didn't see issues.

Snowflake technical support is good. At my previous company, they had a customer success officer for critical accounts, and they were accessible. When I registered as a partner, I also got a quick response from support. I would rate their support eight out of 10.

Positive

Setting up Snowflake isn't complex. I've not seen any challenges.

They have different pricing structures based on data usage. I think they have three or four tiers you can scale through. Most of the clients see an advantage from this type of licensing. That's the reason they go for it. It's priced competitively with other solutions.

I rate Snowflake eight out of 10. 

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 used for data warehousing.

The most valuable features of Snowflake are its performance and power.

Snowflake could improve if they had an Operational Data Store(ODS) space.

I have been using Snowflake for approximately four years.

The stability of Snowflake is good.

The scalability of Snowflake is very good.

I have approximately six customers using this solution but in addition, I am directing more.

The technical support from Snowflake was poor previously, they have improved significantly.

Snowflake has an easy setup and it is quick.

There is a license needed to use this solution. There are a few licensing options available. They have a pay-as-you-go option, but it is recommended to pay upfront.

I would recommend this solution to others.

I rate Snowflake a nine out of ten.

I use this solution to create a proof of concept. I also create training for companies on Snowflake.

It is a very easy-to-use solution. It is user-friendly, and its setup time is very less.

They have a new console, but I couldn't figure out anything in the new console. So, if I shift to the old console, I can figure out where to create the database schema and other things, but I have no idea where to go in the new console. That's one thing they can improve. I don't know why they created a new console to confuse. The old, classic console is much better.

I started using Snowflake three months ago. 

It is stable.

It is scalable.

It is so easy that I didn't have to look for technical support. Its documentation is amazingly simple.

I also use Databricks for proof of concept and training. I have used Databricks much longer than Snowflake. Both have their own pros and cons.

In terms of ease of use, UI, and setup time, Snowflake is good. I would rate Snowflake a nine out of 10 from these aspects. In terms of workload, Databricks has higher points. The underlying infrastructure is faster for Databricks, which is not the case with Snowflake. Snowflake is a cloud database. So, in terms of processing power, Databricks has an advantage over Snowflake. Databricks is more suitable for larger workloads, whereas, for a regular or typical data warehouse that you want to run on the cloud, Snowflake is more suitable. 

Its setup was easy. You can create a connection and be up and running within half an hour.

I set it up myself.

I have not been billed yet, but it should be less. I'm still running the trial version, but it seems to be less than Databricks.

You must try it out. It is a wonderful product. I would also recommend trying out other products, such as Yellowbrick, and doing a comparison. Redshift has also come up with some serverless options. BigQuery is also there. BigQuery is as easy as Snowflake. So, my recommendation or advice is to try out these things and then pick the one that suits you. I don't have any bias towards any of the products. I have an impartial opinion about all the cloud data warehousing products.

I would rate it a nine out of 10.

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.