End-to-End Data Engineering with Snowflake Snowpark (Python)

Introduction

In this Project, I will leverage Snowpark's File API and DataFrame API to process and analyze Amazon sales order data for mobile products, demonstrating the full capabilities of Snowflake in handling large datasets and complex transformations.

Getting Started

Before beginning, ensure you have access to a Snowflake account and have set up Snowpark for Python on your local machine. Familiarity with SQL and Python will be beneficial.

Project Overview

1. Loading Large Datasets from Local Machine to Snowflake Internal Stages

Learn how to efficiently upload large volumes of data from your local environment to Snowflake's managed storage layer (internal stages), preparing the data for further processing and analysis.

Key Steps:

• Prepare your local dataset for upload.
• Use Snowflake's PUT command to transfer data files to an internal stage.

2. Incremental Data Loading (Delta Loads)

Understand the process of incrementally loading new or changed data (delta loads) from your local machine to Snowflake to keep your datasets up-to-date with minimal data transfer.

Key Steps:

• Identify and segregate new or modified data entries.
• Upload the delta data to the internal stage using optimal strategies to reduce load time and resource consumption.

3. Utilizing Snowpark's File API for Data Loading

Explore the capabilities of Snowpark's File API to load data into Snowflake, enabling seamless integration and automation within your Python code.

Key Steps:

• Use Snowpark's File API methods to interact with internal stages and tables.
• Automate the data loading process with Python scripting.

4. Advanced Data Transformations with Snowpark DataFrame API

Delve into complex data transformations using Snowpark's DataFrame API, leveraging the power of Snowflake's computing resources while writing Pythonic code.

Key Steps:

• Perform data cleaning, aggregation, and transformation operations.
• Leverage Snowpark's DataFrame API to execute SQL-like operations efficiently.

5. Creating Dashboards with Snowsight

Finally, visualize your processed data by building a simple dashboard using Snowsight, Snowflake's web-based UI, enhancing data insights with interactive filters and visualizations.

Key Steps:

• Access Snowsight in your Snowflake console.
• Design and configure a dashboard to display key metrics and trends from your data.
• Implement filters for dynamic data exploration.

Conclusion

A brief about using Snowpark API to complete an End-to-End ETL project.