In the cloud, we have various databases like Cosmos DB, customer databases, blob storage, and so on. We don't want to handle everything as JSON files. If data needs to be in a structured format, Snowflake is a good option because it's easy to deploy.
Additionally, Snowflake takes care of the underlying storage layer, offers good performance, and has a very good distributed architecture through virtual warehouses.
Plus, there's the option for security, handling data duplicates, and data governance – it's all in one package. And it can seamlessly integrate with streaming data, which is awesome.
And it also integrates with Python and machine learning, similar to Databricks now. So, it's a good competitor for Databricks.
Snowflake has enhanced our data warehousing capabilities. Mainly, it's the infrastructure. We can distribute data and create a multi-warehouse architecture for performance optimization.
We don't need to worry about memory or storage, and Snowflake Cloud handles licensing and storage. The integration with Azure lets us deploy data seamlessly from our data lake to Snowflake storage using copy scripts with transformations.
Moreover, we primarily use the data sharing feature to store our logs, especially for machine learning use cases where structured data is essential.
Previously, we used Hive with MongoDB integration and a Hadoop layer. Now, we use Informatica to load unstructured data into Snowflake as a target. Then, we convert it into a structured format, making it usable for machine learning.
Snowflake's integrated features actually made the most significant impact on our analytics efficiency. Security, dynamic data masking, data cloning, data sharing, data sampling, ETL processes, and streaming processes – it's all in one product.
It's also a good product for batch processing. While Databricks may be superior for machine learning, Snowflake is still maturing in that area.
Moreover, Snowflake's performance speed was crucial to our project success when it comes to reporting capabilities. We use MicroStrategy as our centralized reporting front-end, which relies on Snowflake.
Also, when migrating data to a data warehouse, we can directly load it into Snowflake using Snowflake's integration with Azure Blob or AWS S3, enabling the infrastructure through Informatica or Azure Data Factory (ADF). This allows for a simple architecture with Snowflake functioning as a data lake, an intermediate transform version, or a source for sector data. And there's no complexity in handling JSON data.
Handling JSON files can be very complex, especially for hierarchical data, which needs to be split. Snowflake makes this very easy to handle.
