Is Snowflake SQL or NoSQL?

Snowflake is SQL-based, meaning it operates primarily as a relational database and supports SQL for querying and managing data. However, it also has advanced capabilities that allow it to handle semi-structured data, similar to what you'd find in NoSQL databases.

How Snowflake blends SQL and NoSQL features:

1. SQL-based platform
   Snowflake is built around SQL, and all interactions with the platform, including querying, updating, and managing data, are done using SQL. This makes it a traditional relational database system.

2. Support for semi-structured data
   Although it is SQL-based, Snowflake can natively handle semi-structured data formats like JSON, Avro, ORC, and Parquet. This makes Snowflake flexible, enabling users to store and query semi-structured data without requiring predefined schemas, which is a common characteristic of NoSQL databases.

3. Schema flexibility
   Snowflake allows schema-on-read functionality, which means you can load semi-structured data without having to define a strict schema upfront. You can later query this data using SQL, giving you some of the flexibility seen in NoSQL systems while maintaining SQL's power and structure.

4. Best of both worlds
   Snowflake combines the power of SQL with the flexibility to handle semi-structured and structured data, making it a hybrid system. However, it is not a traditional NoSQL database like MongoDB or Cassandra, which are designed to be schema-less and handle unstructured data more flexibly.

Suggested resources:

• Grokking the System Design Interview - Useful for understanding how to design data systems that handle both structured and semi-structured data.
• Grokking Data Structures & Algorithms for Coding Interviews - Helpful for mastering problem-solving skills, essential for both SQL and NoSQL data systems.

In summary, Snowflake is fundamentally a SQL-based platform with the ability to handle semi-structured data, giving it some characteristics typically associated with NoSQL databases.