Data Transformations

Data transformations are the backbone of analytics pipelines, but manual processes create bottlenecks. Vinay, a data engineer at GlobalMart, faced a common challenge: tracking changed data, updating reports whenever business rules changed, and managing files across stages—all without a scalable automation framework.

Without proper change tracking and automation, data teams waste hours debugging missing updates, manually refreshing reports, and verifying file uploads. These inefficiencies delay decision-making and increase error rates. Poor data transformation practices can lead to incomplete reports, missed SLAs, and unreliable analytics that undermine business confidence.

This masterclass uses real-world scenarios featuring conversations between Vinay and Rahul, the Senior Architect, to teach Snowflake's transformation capabilities. Through interactive examples, SQL implementations, and hands-on exercises, you'll learn to automate workflows, process semi-structured data, and implement change data capture.

What You'll Learn:

Automation & Scheduling

• Automate SQL execution with Snowflake Tasks using interval-based, CRON-based, and serverless scheduling
• Build task dependencies as DAGs to orchestrate complex workflows with sequential execution
• Activate, modify, and manage task lifecycles without external schedulers

Semi-Structured Data Processing

• Store and query JSON, Avro, ORC, Parquet, and XML using VARIANT, ARRAY, and OBJECT data types
• Access nested data with dot and bracket notation, plus type casting for transformations
• Use FLATTEN, GET_JSON_OBJECT, PARSE_JSON, ARRAY_CONSTRUCT, and OBJECT_CONSTRUCT for data manipulation

Functions & Stored Procedures

• Create User-Defined Functions (UDFs) for reusable calculations across queries
• Build stored procedures for multi-statement automation with Caller's Rights and Owner's Rights
• Replace repetitive manual logic with centralized, maintainable code

Change Data Capture & Sampling

• Implement Streams to track inserts, updates, and deletes for incremental processing
• Distinguish between Standard, Append-Only, and Insert-Only streams for different use cases
• Apply BERNOULLI, SYSTEM, and fixed-size sampling for efficient data extraction

File & Metadata Management

• Generate secure file URLs with BUILD_SCOPED_FILE_URL, BUILD_STAGE_FILE_URL, and GET_PRESIGNED_URL
• Track file metadata using Directory Tables to verify uploads and debug missing files

By the end, you'll understand Snowflake's transformation features—so you can automate data workflows, process complex data structures, and implement reliable change tracking. Test your knowledge throughout with scenario-based questions.