Snowpark use case : For Snowflake users

Discover how Snowpark, Snowflake Data Warehouse's powerful developer experience, revolutionizes data engineering tasks by enabling complex data transformations using popular programming languages.


Jatin Solanki

Updated on

February 2, 2024


Hey fellow data geeks! If you've been working with Snowflake Data Warehouse, I'm pretty sure you've heard of Snowpark. If you haven't, well, it's time to level up your game. Snowpark is a powerful developer experience that enables you to write complex data transformation logic in your favorite programming language, like Python or Scala, and run it directly in Snowflake. In this multi-part article, I'm going to walk you through some challenging yet easy-to-understand use cases and code snippets to show you how to leverage Snowpark for your data engineering needs. So, grab your favorite beverage and let's dive into the world of Snowpark!

What is Snowpark?

Snowpark is a powerful developer experience designed to work with Snowflake Data Warehouse. It allows data engineers and developers to write complex data transformation logic using their favorite programming languages, such as Python, Scala, and Java, and run it directly within Snowflake. Snowpark simplifies the process of building, testing, and deploying data pipelines, enabling developers to focus on their business logic rather than managing infrastructure.

Some key features of Snowpark include:

  1. Support for multiple programming languages: Snowpark allows developers to write their data transformation logic in popular languages like Python, Scala, and Java, catering to a wide range of skill sets.
  2. Seamless integration with Snowflake: Snowpark is designed to work natively with Snowflake, enabling developers to use Snowflake features like virtual warehouses, stages, and materialized views.
  3. Powerful data transformation capabilities: Snowpark provides a rich set of functions and operations to perform complex data transformations, such as aggregations, window functions, and user-defined functions, directly within Snowflake.
  4. Improved performance and cost efficiency: By running data transformations within Snowflake, Snowpark eliminates the need for transferring large volumes of data between Snowflake and external processing engines, resulting in reduced data movement, improved performance, and lower costs.

Overall, Snowpark offers a versatile and powerful solution to supercharge your data engineering tasks within the Snowflake Data Warehouse ecosystem.

Part 1: Data Ingestion and Transformation with Snowpark

1.1 Setting up Snowpark and connecting to Snowflake

Let's start by setting up Snowpark and connecting it to your Snowflake instance. For this example, we'll use Python. First, you'll need to install the snowflake-connector-python and snowpark packages:

Now, let's connect to Snowflake:

1.2 Data Ingestion

Let's say we have a CSV file with sales data that we want to ingest into Snowflake. We can use Snowpark to create a table and load the data:

1.3 Data Transformation with Snowpark

Now that we have the sales data in Snowflake, let's use Snowpark to perform some transformations. We'll start by calculating the total revenue per product.

With this code, we've created a new table total_revenue_by_product in Snowflake, which holds the total revenue per product. This example demonstrates how easy it is to perform complex data transformations using Snowpark.

1.4 Advanced Data Transformation: Calculating Rolling Averages

For this example, let's calculate the 7-day rolling average of revenue for each product. We'll be using window functions to achieve this.

Conclusion (Part 1):

In this first part of the article series, we've explored how to set up Snowpark, connect to Snowflake, and perform data ingestion and transformation tasks. We've seen how Snowpark enables us to write complex data transformation logic using Python and run it directly in Snowflake, making the process more efficient and flexible. In the next part of the series, we'll dive deeper into more advanced use cases and discuss how Snowpark can be used to optimize your data engineering workflow even further. So, stay tuned for more geeky data engineering adventures with Snowpark and Snowflake!

Part 2: Advanced Use Cases with Snowpark

2.1 Creating and Using User-Defined Functions (UDFs)

UDFs can be a powerful tool for extending Snowflake's built-in SQL capabilities and adding custom logic to your data transformations. In this example, we'll create a UDF to calculate the price per unit for each sale, and then use it in a Snowpark query.

First, let's create the UDF in Snowflake:

Now that we have the UDF, let's use it in a Snowpark query:

With this example, we've demonstrated how to create a UDF in Snowflake and use it within a Snowpark query to perform custom data transformations.

2.2 Query Optimization with Materialized Views

Materialized views can significantly improve the performance of your queries by precomputing and caching the results. In this example, let's create a materialized view for the total revenue per product, which we calculated in Part 1.

First, create the materialized view in Snowflake:

Now, let's see how to use this materialized view in a Snowpark query:

By using a materialized view, the top_selling_products query runs faster, as it leverages the precomputed and cached results.

Conclusion (Part 2):

In this second part of the series, we've explored advanced use cases of Snowpark, such as working with user-defined functions and optimizing query performance with materialized views. These examples demonstrate the versatility and power of Snowpark when it

External References:
  1. Snowflake Documentation - Snowpark: Official Snowflake documentation on Snowpark, offering an overview, examples, and detailed guidance on using Snowpark with different programming languages:
  2. Snowflake Documentation - Data Loading: A comprehensive guide on data loading in Snowflake, which includes information on stages, loading data from various sources, and managing data files:
  3. Snowflake Documentation - Data Transformation with SQL: An extensive guide on using SQL for data transformation in Snowflake, covering topics like aggregate functions, window functions, and more:

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