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
Inclusions
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:Â 858-208-4141Â or email:Â training@ccslearningacademy.com; sales@ccslearningacademy.com