Snowpark an Introduction


Before entering into snowpark, we need to know what is Snowflake.

Snowflake is a high-performance, scalable, and fully-managed data warehousing solution. It provides a number of other data integration and processing functions, including data sharing, data interchange, and machine learning, in addition to allowing users to store, query, and analyze their data using normal SQL commands.

Let’s compare before and after Snowpark:

 Snowflake does provide some support for unstructured data, such as semi-structured or XML files; it is not typically used as a primary storage location for large files like documents, images, or videos. So people usually use Amazon S3, Microsoft Azure Blob Storage, or Google Cloud Storage, and then store the metadata and file locations in Snowflake to enable querying and analysis is the option snowflake had.

This was the strategy followed earlier.

You can store, access, process and govern unstructured data in Snowflake. With built-in capabilities to store and assess unstructured data characteristics like key metadata, versioning, and migration, you can use the most appropriate tool for each piece of data. 

Okay, you have the option to store unstructured data but imagine you have only the option to process them using SQL and not be able to process them with some complex logic within the same snowflake environment as your wish. Instead of moving them to external cloud resources like AWS, Azure, etc, process them and return the result back to Snowflake. So here the data movement needs to perform and thereby cause performance and security issues.

Not only for unstructured data, but if you need to write complex logics/functionalities you need to depend on external cloud resources

Here’s an example: Before Snowpark, people used a Snowflake connector to combine snowflakes with spark code. As we know spark provides various options to process structured/unstructured data in a distributed manner.

Here if you need to use spark, you need to take care of the complexity of creating and managing clusters(which is also time-consuming), By default, Spark is insecure hence explicitly need to set up security configurations and need more knowledge to troubleshoot the gaps.

Here comes the importance of SnowPark.

What is Snowpark?

Snowpark is a feature introduced by Snowflake that enables data engineers, data scientists, and developers to use Python, Scala, and Java with full control over libraries to build processes. So now you don’t need to rely on writing complex SQL queries. It also provides the benefits of spark with none of the above-mentioned complexities. Here 

Instead of exporting data to execute in other contexts, Snowpark enables developers to take advantage of Snowflake’s computational power by shipping their code to the data. This might significantly improve things.

Some Advantages:

  • Reduce Security Risks: You do not need to worry about security risks, Snowflake provides high-security features and governance.
  • No unnecessary data pipelines or Spark-based setups to incur infrastructure and operational expenditures.
  • Keep all in one place: One platform that natively supports everyone’s preferred programming language and constructs will enable all teams to interact on the same data (Eg: Data engineers, ML Team, etc).
  • You can enjoy all features and performance advantages of Snowflake.


    No need to worry about Data processing or including complex logic as your wish, Snowpark will help you. And also you can enjoy the lightning-fast snowflakes performance along with in-built high-security features. So you do not need to worry about configurations. Just focus on your Data.

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