Snowflake Reviews

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.

We are using it for optimizing costs and using financial data.

It is a cloud solution with many useful features. It has the data science capability. It can transform data and prepare data for a data science project with scalability.

We would like to be able to do modeling with Snowflake. It should support statistical modeling.

I have been using this solution for more than a year.

It is stable.

It is very scalable. We currently have more than 50 users. We can increase its usage.

I have not contacted their support.

I used Cloudera One about two years ago.

It is easy. It is a SaaS solution, so there is no installation. Therefore, you don't need the infrastructure team, but you need a few data analysts.

It is pay-as-you-go. Its cost is in the medium range.

I would rate it a 10 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 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.