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Overview
This three-day role-specific course covers key concepts, features, considerations, and Snowflake recommended best practices through the lens of the data engineering workflow. It is intended for participants who will be accessing, developing, and querying datasets for analytic tasks and building data pipelines in Snowflake. This course consists of core data engineering concepts delivered through lectures, demos, labs, and discussions.
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 effectively 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.
Data Analysts Data Engineers Data Scientists Database Architects Database Administrators Data Application Developers
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Module 1: Snowflake AI Data Cloud Module 2: Introduction to the Data Engineering Workflow Module 3: Supporting Platform Features Authentication Methods Drivers, Clients, and Connectors Overview Role-based Access Control (RBAC) Overview Introduction to Data Governance QueryTags Module 4: Data Storage Semi-structured Data QuerySemi-structured Data Data Lake Apache Iceberg™ Tables External Tables Module 5: Ingestion 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 SchemaDetection Working with Unstructured Data Module 6: Transformation Extensibility Overview Snowflake Scripting UDFsandUDTFs Extend Snowflake with Java and Python External Functions External Network Access Data Sharing Introduction to Snowpark Working with Snowflake Notebooks Transformations with Unstructured Data Module 7: Powering Data with Snowflake LLMs Document AI Cortex LLM Functions Overview Cortex LLM Task-specific Functions Cortex LLM Complete Functions Cost Monitoring Module 8: Orchestration Creating and Managing Tasks Creating and Managing Streams Streams on Views Using Streams and Tasks Together Dynamic Tables Module 9: Performance Optimization Natural Clustering Explicit Clustering Automatic Clustering Service Search Optimization Service SQLPerformance Tips Performance Bottleneck Scenarios Module 10: Delivery Materialized Views Unloading Semi-structured Data Secure Views Module 11: Management and Observability Observability on Snowflake Outbound Notifications Snowflake Alerts Data Metric Functions System DMF CustomDMF Observability Within Snowsight Cost Controls Resource Monitors
Module 1: Snowflake AI Data Cloud
Module 2: Introduction to the Data Engineering Workflow
Module 3: Supporting Platform Features
Module 4: Data Storage
Module 5: Ingestion
Module 6: Transformation
Module 7: Powering Data with Snowflake LLMs
Module 8: Orchestration
Module 9: Performance Optimization
Module 10: Delivery
Module 11: Management and Observability
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