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.
Snowflake AI Data Cloud
SnowflakeExternal reviews
External reviews are not included in the AWS star rating for the product.
Was fine until the last update
Impressive Data Storage, but Lacks Integrated Infrastructure
Snowflake an excellent data cloud platform
Snowflake: An Innovate Solution
Provides good data ingestion capability, but should include more AI capabilities
What is our primary use case?
What is most valuable?
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.
What needs improvement?
The addition of more AI capabilities in Snowflake would help us more.
For how long have I used the solution?
I have been using Snowflake for two years.
What do I think about the stability of the solution?
To the extent I use it, Snowflake is a very stable solution. I did not find any instability with the tool because we primarily use it to create our data lake, and it is available.
What do I think about the scalability of the solution?
Snowflake is a scalable solution. Depending on your needs, you can scale it up or down. I find it quite flexible in terms of scalability. Around 50 users work with Snowflake in my process.
Which solution did I use previously and why did I switch?
I also used Oracle. Snowflake is easier to handle than other solutions and has many things under one umbrella. I like Snowflake's data ingestion capability the most compared to other RDS and database vendors. In other solutions, you would need to design your integration methods separately. However, if you choose, it's not fake. Data ingestion and data lake creation can be easily achievable with Snowflake.
What's my experience with pricing, setup cost, and licensing?
Oracle is less expensive than Snowflake. Snowflake provides better value at a little higher cost in the Snowflake. RDS is cost-effective but has fewer features than Snowflake.
What other advice do I have?
The solution's integration aspect is good, and all the connectors are in place. I found Snowflake similar to RDS. We use it for both data in motion and data in transit. It looks like the tool handles the data quite securely.
We create ETL patterns. We ingest data from different source systems, and we have to create data pipelines. It would be useful if we could have AI features added to identify what I'm going to do with this data. It would be good if it could look at the data and help me create an automated pipeline instead of me creating a pipeline by myself.
I'm from a retail background. I completed my Oracle DBA training a long time ago, about 18 years ago. I was quite familiar with the Snowflake and relational database concepts since I had already completed the Oracle ops, DBA ops, OCP, and OPA courses. For me, it was a journey similar to when I shifted from Oracle RDS to Snowflake. Although I was quite familiar with most of the concepts, there were some learnings.
Whosoever is in the data field should at least try Snowflake once. They will then realize the best features in the solution and can continue using it.
Overall, I rate the solution a seven out of ten.
Generates metrics efficiently, but the integration process needs enhancement
What is most valuable?
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.
What needs improvement?
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.
What do I think about the scalability of the solution?
The product's scalability is crucial for managing petabyte-scale data generated daily across various regions, allowing for efficient data validation and handling.
How was the initial setup?
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.
What other advice do I have?
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.