Snowflake Data Engineer Training

Start Date: January 01, 1970 – 12:00 AM (Pacific Time)

End Date: January 01, 1970 – 12:00 AM (Pacific Time)


*Looking for flexible schedule (after hours or weekend)? Please call or email us: 858-208-4141 or
Student financing options are available.

Transitioning military and Veterans, please contact us to sign up for a free consultation on training and hiring options.

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Course Description:

This 3-day role specific course covers the Snowflake key concepts, features, considerations, and best practices intended for key stakeholders who will be accessing, developing, querying datasets for analytic tasks and building data pipelines in Snowflake. These stakeholders often are in the role of database application developer and data engineer. This course will consist of lectures, demos, labs, and discussions.

Course Outline

Snowflake Architecture and Overview
• Snowflake technical overview
• Review three-tiered architecture
• Cloud services
• Virtual warehouse
• Storage

Interfaces and Connectivity
• Using Snowflake web console, worksheet
• Using Snowflake command line, SnowSQL
• Overview of connectors and ecosystem for Snowflake

Developing for Snowflake
• Overview of programmatic interfaces for Snowflake
• A deeper look at a specific programming interface such as Python and Spark
• Stored procedures
• External functions
• User defined functions

Loading New Data Sets
• Ingesting new data into Snowflake tables
• Working with various SQL data types
• Ingestion Snowflake best practices

Data Pipelines
• Continuous data processing using tasks
• Continuous data ingestion using SnowPipe
• Continuous change processing using streams
• Streaming connectors, such as Kafka for continuous ingestion

Data Lakes
• External tables and data lakes
• Partitioning for eecient queries over external files
• Materialized views sourced from external tables

Pipeline Transformations and Querying Data
• Review best practices of writing eective queries
• Filtering data examples and best practices
• Walk through grouping, rollups, and sorting data and performance considerations
• Usage and pitfalls of joining data
• Using Snowflake’s high performing approximation and estimation functions
• CTEs and analytic functions

Query Caching Performance Features
• Result set cache
• Metadata cache
• Query data cache
• Best practices of using caching for performance and cost optimization

Performance monitoring and management of query and ETL workloads
• Query profiling
• Virtual warehouse (compute resource) management
• Optimizing and tuning workloads
• Monitoring functions and cost management

Using Data Clustering Optimization for Advanced Query Performance Tuning
• How to identify appropriate use cases
• Designing clustering keys
• Auto-clustering service
• DML considerations
• Materialized views
• Search optimization service

Test, QA, and Production and Agile Development
• Time travel queries in Snowflake
• Cloning data and environment in Snowflake

Working with Semi-Structured Data
• Data source formats
• Support of native data types
• SQL operations (grouping, sorting and more)
• Built-in functions for traversing, flattening, and nesting of semi-structured data

Snowflake Data Cloud
• Data sharing Overview
• Snowflake Data Exchange and Snowflake Data Marketplace
• Secure views and UDFs

Course Objectives

By the end of this course, you will learn:

  • Overview of Snowflake key features and architecture
  • Performance and cost optimization techniques using caching and high performing functions
  • Learn different UI and application methods of accessing Snowflake
  • Use the capabilities and best practices for working with semi-structured data in Snowflake Load, unload data sets and best practices
  • Tune queries and performance using advanced techniques such as data clustering and materialized views
  • Develop application for Snowflake including using comprehensive ANSI standard SQL support
  • Leveraging Snowflake SQL extensibility features such as time travel capabilities, user-defined functions and stored procedures

Target Audience

  • Who should attend this course?
    • Data Analysts
    • Data Engineers
    • Data Scientists
    • Database Architects
    • Database Administrators


Basic knowledge of SQL and Python is helpful.


With CCS Learning Academy, you’ll receive:

  • Instructor-led training
  • Training Seminar Student Handbook
  • Pre and Post assessments/evaluations
  • Collaboration with classmates (not currently available for self-paced course)
  • Real-world learning activities and scenarios
  • Exam scheduling support*
  • Enjoy job placement assistance for the first 12 months after course completion.
  • This course is eligible for CCS Learning Academy’s Learn and Earn Program: get a tuition fee refund of up to 50% if you are placed in a job through CCS Global Tech’s Placement Division*
  • Government and Private pricing available.*

*For more details call: 858-208-4141 or email:;