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The Snowflake Data Cloud is a cloud-based data warehousing platform that allows businesses to store and query data for analytical purposes.

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Snowflake’s Data Cloud is powered by an advanced data platform provided as a self-managed service. Snowflake enables data storage, processing, and analytic solutions that are faster, easier to use, and far more flexible than traditional offerings.

The Snowflake data platform is not built on any existing database technology or “big data” software platforms such as Hadoop. Instead, Snowflake combines a completely new SQL query engine with an innovative architecture natively designed for the cloud. To the user, Snowflake provides all of the functionality of an enterprise analytic database, along with many additional special features and unique capabilities.

Key features

  1. Separation of Storage and Compute: Unlike traditional databases, Snowflake allows us to scale storage and compute resources independently.
  2. Multi-Cloud Compatibility: Snowflake runs on all major cloud platforms (AWS, Google Cloud, Azure). In our case, we use AWS, but Snowflake makes it easy to move between platforms if needed — giving us flexibility in our cloud strategy.
  3. Support for Structured and Semi-Structured Data: We deal with data in multiple formats (CSV, JSON, Parquet), and Snowflake’s native support for semi-structured data allows us to load and query this data easily, without complex transformations.
  4. Automatic Scaling and High Performance: One of the most valuable features in my project is Snowflake’s automatic scaling. When more users access the data or run complex queries, Snowflake automatically scales up the computing power to handle the load. Once the job is done, it scales back down, ensuring we only pay for what we use.

Snowflake stages

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In Snowflake, a stage represents a named object that acts as a temporary holding area for data files being loaded into a Snowflake table. The data can be stored in either Snowflake internal storage or external storage services like Amazon S3 or Microsoft Azure. A stage provides a way to manage and control access to the data being loaded into Snowflake.

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Best Practices for Managing Snowflake Stages

  1. Create a stage for each external data source.
  2. Use the Snowflake table staging process.
  3. Leverage the Snowflake COPY INTO the stage table process.

See Best Practices for Managing Snowflake Stages

Snowflake stages

Resize and Use a Warehouse for Data Loading