Snowflake Project 5: Handling Semi-Structured Data

This project demonstrates how to work with semi-structured data formats in Snowflake, specifically JSON and Parquet. The exercises cover:

• Loading JSON data into Snowflake and extracting structured information
• Loading Parquet data efficiently from S3
• Using Snowflake stages to manage external data
• Querying and validating semi-structured data

The objective is to show how Snowflake can handle a mix of structured and semi-structured data formats for analytics and reporting.

---

Prerequisites

• Active Snowflake account
• Access to Snowflake Web UI or SnowSQL
• AWS credentials with access to the specified S3 buckets
• Demo warehouse created in Project 1

---

Tasks Performed

TASK 1: Loading JSON Data

• Created a database and table to store JSON data using a VARIANT column
• Created an external stage pointing to the JSON file on S3
• Loaded the JSON file into the Snowflake table using the stage
• Verified successful data load
• Queried and flattened the JSON data to extract structured values for analysis

---

TASK 2: Loading Parquet Data

• Created a database and table to store Parquet data
• Created an external stage pointing to the Parquet file on S3
• Verified the file contents in the stage
• Loaded the Parquet data into Snowflake
• Verified successful data load and queried the table

---

Real-World Relevance

• JSON and Parquet support allows Snowflake to handle semi-structured data commonly used in modern data pipelines
• VARIANT columns and FLATTEN enable transforming semi-structured data into a structured format for analytics
• External stages provide a scalable way to manage cloud-based datasets without manual file transfers