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Course Overview
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.
Who Should Attend
- Data Analysts
- Data Engineers
- Data Scientists
- Database Architects
- Database Administrators
- Data Application Developers
Course Objectives
- Describe the data engineering workflow and how the Snowflake AI Data Cloud features support the various components of the workflow.
- Access Snowflake through the Snowsight UI and by using application methods.
- Load and unload data sets.
- Configure Snowflake features to cover a range of data ingestion and processing latencies.
- Develop applications for Snowflake, including comprehensive ANSI standard SQL support.
- Employ performance and cost optimization techniques.
- Use Snowflake’s capabilities to work eectively with structured, semi-structured, and unstructured data in Snowflake.
- Tune queries and improve performance using advanced techniques such as data clustering and materialized views.
- Employ Snowflake SQL extensibility features such as user-defined functions and stored procedures.
Course Outline
1 - Snowflake Data Cloud
2 - Introduction to the Data Engineering Workflow
3 - Supporting Platform Features
- Authentication Methods
- Drivers, Clients, and Connectors Overview
- Snowflake Connector for Python
- SnowSQL
- Role-based Access Control (RBAC) Overview
- Introduction to Data Governance
4 - Data Storage
- Semi-structured Data
- Query Semi-structured Data
- Query Tags
- Data Lake
- Apache Iceberg Tables
- External Tables
5 - Powering Data With Snowflake LLMs
- Document AI
- Cortex LLM Functions Overview
- Cortex LLM Functions Specialized Functions
- Cortex LLM Functions Complete
- Cost Monitoring
6 - Ingestion Layer
- Bulk vs. Continuous Data Loading Approaches
- Snowpipe
- Snowpipe Streaming
- Snowflake Connector for Kafka
- Snowflake Connector for Kafka With Snowpipe Streaming
- Snowflake Data Loading Best Practices
- Loading Semi-structured Data
- Schema Detection
- Working With Unstructured Data
- Creating and Managing Streams
- Streams on Views
7 - Orchestration
- Creating and Managing Tasks
- Using Streams and Tasks Together
8 - Transformation
- Dynamic Tables
- Extensibility Overview
- Snowflake Scripting
- UDFs and UDTFs
- Extend Snowflake With Java and Python
- External Functions
- External Network Access
- Introduction to Snowpark
- Transformations With Unstructured Data
9 - Performance Optimization
- Natural Clustering
- Explicit Clustering
- Automatic Clustering Service
- Search Optimization Service Introduction
- SQL Performance Tips
- Performance Bottleneck Scenarios
10 - Modeled Layer
- Materialized Views
- Unloading Semi-structured Data
- Data Sharing
- Secure Views
11 - Management and Observability
- Observability on Snowflake
- Outbound Notifications
- Snowflake Alerts
- Data Metric Functions
- System DMF
- Custom DMF
- Observability Within Snowsight
- Cost Controls
- Resource Monitors
- Working With JupyterLabs