Snowflake Reviews

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.

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.

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.