Overview
Master Snowflake data engineering through hands-on exercises covering data ingestion, transformations, pipeline orchestration, DevOps practices, and observability to build practical skills for real-world projects.
Syllabus
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- Introduction to Snowflake
-- Overview of Snowflake Architecture
-- Cloud Data Warehousing Concepts
-- Setting Up Your Snowflake Environment
- Ingesting Data at Scale
-- Data Loading Techniques
-- Using Snowpipe for Continuous Data Ingestion
-- Handling Semi-Structured and Structured Data
- Data Transformations with SQL and Python
-- SQL Transformations in Snowflake
-- Integrating Python for Data Manipulation
-- Utilizing Snowflake's Ecosystem for Advanced Transformations
- Delivering Data Products
-- Data Warehousing Best Practices
-- Creating Views and Tables for Data Products
-- Building Dashboards for Data Visualization
- Orchestrating Data Pipelines
-- Introduction to Data Pipelines
-- Utilizing Snowflake Tasks and Streams
-- Scheduling and Automating Workflows
- Implementing DevOps Best Practices
-- Version Control and CI/CD for Data Pipelines
-- Managing Environments and Deployments
-- Ensuring Data Quality with Testing Methods
- Observability of Data Pipelines
-- Monitoring and Logging in Snowflake
-- Implementing Alerts and Notifications
-- Performance Optimization and Tuning
- Hands-On Labs and Projects
-- Real-world Scenarios and Challenges
-- Collaborative Projects
-- Capstone Project: Building a Comprehensive Data Engineering Solution
- Course Wrap-Up
-- Review Key Concepts
-- Future Trends in Data Engineering and Snowflake
-- Career Pathways and Resources
Taught by
Tags
On-Demand
