Snowflake Data Engineer
Leveraging Business Intelligence: Advanced Data...
Snowflake Data Engineer
Leveraging Business Intelligence: Advanced Data Analysis, Reporting, and Optimization Using Snowflake AI Data Cloud
Type
Paid
$1,275.00
August 26 @ 9:00 am – August 28 @ 5:00 pm UTC+5:30
About the event
Join us for a comprehensive three-day, hands-on training designed for professionals who work with data engineering workflows on Snowflake. This role-specific course dives deep into data ingestion, transformation, pipeline development, and performance optimization using Snowflake’s powerful features and AI Data Cloud capabilities. Through a combination of lectures delivered by Snowflake experts, live demos, practical labs, and peer discussions, participants will gain the skills and confidence to build scalable and efficient data solutions in Snowflake.
What You’ll Learn:
- Understand the end-to-end data engineering workflow on Snowflake
- Access and interact with Snowflake via Snowsight UI and APIs
- Load, transform, and manage data (structured, semi-structured, unstructured)
- Develop scalable data applications with SQL and extensibility features
- Optimize performance using clustering, materialized views, and tuning techniques
- Apply best practices for cost-efficiency and advanced processing
Target Audience:
- Data Engineers
- Data Analysts
- Data Scientists
- Database Architects
- Data Application Developers
- Database Administrators
Prerequisites:
- A foundational understanding of data engineering concepts
- Completion of the “Snowflake Foundations” one-day course or equivalent hands-on Snowflake experience
Snowflake Data Cloud
Introduction to the Data Engineering Workflow
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
Data Storage
- Semi-structured Data
- Query Semi-structured Data
- Query Tags
- Data Lake
- Apache Iceberg Tables
- External Tables
Powering Data With Snowflake LLMs
- Document AI
- Cortex LLM Functions Overview
- Cortex LLM Functions Specialized Functions
- Cortex LLM Functions Complete
- Cost Monitoring
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
Orchestration
- Creating and Managing Tasks
- Using Streams and Tasks Together
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
Performance Optimization
- Natural Clustering
- Explicit Clustering
- Automatic Clustering Service
- Search Optimization Service Introduction
- SQL Performance Tips
- Performance Bottleneck Scenarios
Modeled Layer
- Materialized Views
- Unloading Semi-structured Data
- Data Sharing
- Secure Views
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
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