Unit 1: The Evolving Data Landscape
Unit 2: The Engineering Mindset
Unit 3: The Power of Scripting & Automation
Scripting-Focused Data Engineering for Data Analysts: From SQL to Python ETL
Master the essential scripting and programming skills to transition from data analysis to building robust, automated data pipelines and infrastructure.
Unit 1: Modular Querying with CTEs
Unit 2: Powerful Analytical Functions
Unit 3: Advanced Data Integration
Unit 1: Understanding Query Performance
Unit 2: Indexing Strategies
Unit 3: Optimizing Query Constructs
Unit 4: Advanced Optimization Techniques
Unit 1: Python Basics for Data Engineers
Unit 2: Structuring Python Code
Unit 3: Interacting with Files
Unit 1: Pandas Fundamentals: Your Data Workbench
Unit 2: Data Cleaning & Preparation
Unit 3: Transforming Data with Pandas
Unit 4: Performance & Best Practices
Unit 1: Connecting to Databases
Unit 2: Executing SQL Queries
Unit 3: Efficient Data Movement
Unit 1: ETL Fundamentals
Unit 2: ETL Design Patterns & Best Practices
Unit 3: ETL Challenges & Evolution
Unit 1: Extracting Data with Python
Unit 2: Transforming Data with Python
Unit 3: Loading Data with Python
Unit 4: Structuring ETL Pipelines
Unit 1: Robust Error Handling
Unit 2: Effective Logging Strategies
Unit 3: Data Validation & Quality
Unit 1: Foundations of Data Storage Architectures
Unit 2: Deep Dive into Data Warehouses
Unit 3: Exploring Data Lakes
Unit 4: Warehouses vs. Lakes: A Comparison
Unit 1: Git Fundamentals: Your Code's History Book
Unit 2: Branching & Merging: Parallel Development
Unit 3: Remote Repositories: Collaborative Workflows
Unit 4: Git Best Practices for Data Engineers
Unit 1: Automating Your Pipelines
Unit 2: Exploring Orchestration Tools
Unit 3: Your Data Engineering Journey