Snowflake Reviews

I started working with Snowflake when I was with Fidelity Investments around 2016-2017. We used Snowflake on AWS cloud because Snowflake doesn’t have an on-premise offering. You need to use it with AWS, Azure, or Google Cloud. 

As a consultant now, I assist enterprise customers, though I don't have Snowflake deployments yet.

Snowflake is a data lake on the cloud where all processing happens in memory, resulting in very fast query responses. One key feature is the separation of compute and storage, which eliminates storage limitations. 

It also has tools for migrating data from legacy databases like Oracle. Its stability and efficiency enhance performance greatly. Tools in the AI/ML marketplace are readily available without needing development.

Snowflake is used to create a data lake, and we have a consumer base where we create data. This data is consumed by different consumers and vendors, who have different needs in how they want to use it. We are from the data engineering side, and we put this data from various source systems in this data lake. We put the data into Snowflake and use connectors to connect to the data lake.

With Snowflake, we don't need any other ETL tool, which is the primary reason I started liking this tool. In addition to the database, Snowflake also provides ingestion capabilities. Currently, we are only using the database because we already have integrations with the IICS. The solution also provides data engineering capabilities, which we can leverage and utilize in the future.

The main use cases for Snowflake Data Cloud are standard data warehouses.

Real-time data sharing capabilities are not something we require with Snowflake Data Cloud.

It's easy to use, which is a feature that really helps me out.

Snowflake Data Cloud is scalable enough for my data management processes.

The reduced infrastructure management is the big difference compared to on-prem warehousing with Snowflake Data Cloud.

Snowflake Data Cloud is good enough security-wise for my needs, and it can integrate in terms of networks and user management.

Integration with third-party tools is possible, not just Snowflake Data Cloud solutions.

Mostly, we use it for the data warehousing side of use cases, where you have, like, a huge amount of data, and you are required to do reporting in terms of data science, data warehousing, or ad hoc reporting. The use cases we have used are, for example, data coming from MedTech devices, mostly sensor data, which we need to load in Snowflake and do data analytics. We have been using the tool for a couple of MedTech clients.
 

The most important part of the tool is that computing and storage are totally separated, and it keeps on evolving every two weeks, with the tool having releases. New features are coming up in the tool. With respect to AI, the tool is also progressing well. The scalability and performance are quite good. If you have data, like in CSV or any other format, you can load it very quickly and then do your analysis. Columnar database performance, scalability, and the addition of new features are a few useful features of the tool.

We use the solution extensively for reporting and analytics. We put data into it and publish reports. A report could be a dashboard. The data is used for data-driven insights and AI and MLOps. There are many use cases for the data that we procure on Snowflake.

The product has impacted collaboration across our teams. The correlation is quite easy. We use the solution because most of our life sciences and healthcare customers prefer it. Their preferences directly impact us as vendors and partners. We have to promote the same tools and technology they are familiar with.

The solution is very good for building data warehouses. However, it has some limitations if we need it for more use cases.

All the features of the product that are needed for the data warehouse are good. The solution enables the ingestion of data and the usability of preferred languages while creating the data. The performance of the engine is good. The solution speeds up the process of onboarding. We can connect to different sources and get the data very fast. 

The tool does a very good job in reporting and data transformation. We can adapt it well to our needs. When we try to ingest data from many sources, it helps harmonize the data sources. It also helps with duplication and cleaning of the data. It is a pretty difficult and time-consuming task, and Snowflake helps us with it.

The solution is primarily used for data warehousing. 

It's a good tool. It is quite easy to manage. 

There are a lot of features. 

If you accidentally delete something, you can time travel and retrieve it. 

Within half an hour or even 15 minutes, you can set up a new data warehouse, and you can clone a table or whatever you like. 

It's easy to provide access to clients if they need to.

The solution is stable. 

It can scale well.

There is good online documentation via the community, and you can learn the solution on your own.