Snowflake Reviews

Our aim was to migrate everything from on-premise, so we just migrated as it is and then we had issues. Some use cases that were running on-premises were not installed. We just went through each case and then finalized the issues with some of the packages that were not working or some users that were not getting what they were expecting. We did deep analysis on each and every case and then looked for options in Snowflake and are now working with the team to move everything over to Snowflake. 

The data warehouse is one of the great concepts of Snowflake. The coding plans are also a great feature. You can switch out the values or sizes. 

It would be helpful if implementation could be handled more on the user-side. We need to train the users on best practices and how to use the solution properly. It's a cost issue. If they don't run it properly then it'll end up costing more money.

There are some stored procedures that we've had trouble with. The solution also needs to fine-tune the connectors to be able to connect into the system source.

The solution is stable. We had only one failure, but that was because of AWS issues. Beyond that, I haven't seen anything else. From the Snowflake side, within 10 minutes they reported the AWS issue. It was under two hours of downtime because of the quick response.

As long as you don't need to worry about storage or cost, this solution would be one of the best ones on the market for scalability purposes. We've migrated about 400-450 dealers onto the solution so far. We do intend to expand usage so there will be more users and more data. The drawbacks we've had with on-premises was space being a constraint and the user code having limitations.

Customer support is good. There is always someone who's going to respond. They will let you know what can be done and what is possible.

We did some testing and some comparisons, but it's all set up now and running fine. The deployment took about three months. Since we didn't want to disrupt the on-premises, or overload the system, we did most of the migrations on the weekend.

For maintenance, in the beginning, until you are through with training and performance tuning, you will need more people. You might need to start with seven or so, and then, for ongoing work, probably one or two people can manage it. 

We had a Snowflake consultant on-site that assisted us with the implementation.

It's an expensive solution. We can't predict exactly how much until we've streamlined everything and the user requirements have been completed, but normally they charge on the storage, which depends upon the average storage amount used for the month.

We looked at IBM because it also has on-premise solutions. We also looked at Azure as well as AWS.

Because most of the issues you come across can be dealt with on the user's sites, it's important to educate the users and understand their requirements.

The best advice I can give is to understand the product and to try to stick to what is required. From the business side, you need to monitor usage and monitor the space because of on-premises constraints. If it gets filled up then you will have to react. However, this solution is very scalable. 

I would rate this solution between seven and eight out of ten. The solution still has some constraints that need to be addressed.

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.

We were using MySQL and MongoDB for our regular operations but when we grew, we were forced to handle a huge number of datasets. We could have petabytes of data in and out on a regular basis. We struggled a lot to complete the operations in a timely manner. With Amazon Redshift, we gained a lot in terms of timing, as well as project completion. Even-through we are happy with Redshift, in terms of pricing and availability of nodes, I am more comfortable with Snowflake. Some of the scoring mechanism really works well in the distributed architecture of Snowflake.

• Scalability. This solution has the ability to load a huge number of datasets (I have experience with petabytes of data) and process them. Storage is not limited, and we can increase what we want.
  
• Performance. The distributed architecture of Snowflake has the capacity to process huge datasets faster and allows us to scale up and down according to our needs.
• Flexibility. This feature is helpful for the user to increase the node size and config depending on their need. There is no need to wait for hardware to be in place whenever we increase the dataset. Snowflake provides the option to increase the node or cluster size whenever required.
• Multi-formatted accessibility. The Snowflake engine has the capability to read the following file formats: CSV, DELIMITER, FIXEDWIDTH, AVRO, JSON, BZIP2, GZIP, LZOP. The user can choose which format is best for their requirements.
• Configuration. Even though its running in the AWS platform, it beats their platform product. I feel more flexible with this solution than I do with Redshift.
• Snowpipe. Snowpipe is a better system to feed stream data to the Snowflake database.
  

Snowflake has to improve their spatial components since it doesn't have much in terms of geo-spatial queries.

No

No

It's really good. I am happy with the product and the service.

With Amazon Redshift, we gained a lot in terms of timing as well as project completion. Even through we are happy with Redshift, in terms of pricing and availability of nodes, I am more comfortable with Snowflake.

The initial setup of Snowflake is very simple and straightforward. We do not need to read or understand any of the technical documentation. It is a plug-and-play service or platform.

I have implemented this solution through an in-house team.

I can't directly convey ROI because we are not using only Snowflake. We are using multiple products to increase our revenue and decrease time consumption. This makes it difficult to calculate ROI.

I have experience working in Hadoop & Redshift as well. When I compare the three (Snowflake, Hadoop, and Redshift), Snowflake is more user-friendly in terms of configuration and maintenance.

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 Snowflake as a data migration platform.

Data Science capabilities are the most valuable feature.

To ensure the proper functioning of Snowflake as an MDS, it relies heavily on other partner tools. As a result, our solution architects must devote most of their time to addressing this issue.

I have been using Snowflake for two years.

I give the stability a nine out of ten.

I give the scalability a nine out of ten.

The technical support team is good.

The initial setup is straightforward. Since Snowflake is software, the only requirements to start using it are to select a subscription and activate our account.

I give Snowflake a nine out of ten.

Snowflake is being used at the enterprise level.

If an organization is interested in embarking on its cloud journey and Snowflake fulfills its requirements, I would recommend it as a viable solution.