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, and querying datasets for analytic tasks and building data pipelines in Snowflake. These stakeholders often are in the role of database application developers and data engineers. This course will consist of lectures, demos, labs, and discussions.
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
By the end of this course, you will learn:
Basic knowledge of SQL and Python is helpful.
*For more details call:Â 858-208-4141Â or email:Â training@ccslearningacademy.com; sales@ccslearningacademy.com
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