This foundational course introduces the core principles and practices of data engineering, focusing on the construction and management of robust data pipelines. Participants will gain practical skills in extracting, transforming, and loading (ETL) data from various sources into analytical data stores. We cover essential technologies, data modeling for the warehouse, and best practices for orchestration and quality assurance. This program is essential for anyone responsible for building reliable infrastructure that powers business intelligence and machine learning applications.
Data Engineering Fundamentals: Pipelines and ETL/ELT Processes
Data Analytics and Business Intelligence
October 25, 2025
Introduction
Objectives
Upon completion of this course, participants will be able to:
- Explain the difference between ETL and modern ELT architectures and when to use each.
- Design dimensional data models (star and snowflake schemas) optimized for querying.
- Implement data ingestion techniques for structured, semi-structured, and streaming data.
- Apply Python and essential libraries (Pandas) for efficient data cleansing and transformation.
- Build resilient, fault-tolerant data pipelines using orchestration tools like Apache Airflow.
- Establish effective data quality checks and validation processes within a pipeline.
- Understand the concepts of data governance, lineage, and metadata management.
- Select and use appropriate cloud-native tools (e.g., Fivetran, Stitch) for automated data loading.
Target Audience
- Aspiring Data Engineers
- BI Developers and Analysts seeking to upskill
- Database Administrators (DBAs)
- IT Professionals managing data infrastructure
- Software Developers transitioning to data roles
- Project Managers overseeing data initiatives
Methodology
The training utilizes a practical, code-first approach. **Scenarios** involve building a multi-stage data pipeline from a fictional e-commerce source to a data warehouse. **Case studies** analyze real-world failures due to poor data quality and how robust engineering practices could have prevented them. **Group activities** focus on designing a dimensional model for a new business domain. **Individual exercises** require participants to write Python scripts for data transformation and configure tasks in a simulated Airflow environment. **Syndicate discussions** debate the pros and cons of managed cloud ELT services versus custom-coded pipelines.
Personal Impact
- Gain a foundational, engineering-based approach to solving data problems.
- Acquire proficiency in Python and SQL for pipeline construction and management.
- Build a demonstrable portfolio of ETL/ELT pipeline workflows using modern tools.
- Improve collaboration by understanding the requirements of Data Scientists and Analysts.
- Increase career value by bridging the gap between data sources and business intelligence.
Organizational Impact
- Establish reliable, scalable, and automated data infrastructure organization-wide.
- Significantly improve data trust and quality by implementing consistent validation checks.
- Reduce manual data preparation effort and accelerate time-to-insight for decision-makers.
- Enable advanced analytics and machine learning initiatives with clean, structured data.
- Lower the risk of data breaches or compliance failures through better governance controls.
Course Outline
UNIT 1: The Role of Data Engineering and Architecture
Foundational Concepts- Defining Data Engineering vs. Data Science vs. Data Analysis
- Understanding Data Flow and the Data Ecosystem
- Key Differences between Data Warehouses, Data Marts, and Data Lakes
- Introduction to Modern ELT (Extract, Load, Transform) Architecture
- Choosing the Right Tools for Data Storage (SQL, NoSQL, Columnar)
UNIT 2: Data Modeling for Analytics
Dimensional Modeling and Schema Design- Introduction to Ralph Kimball's Dimensional Modeling (Star Schema)
- Designing Fact Tables and Dimension Tables
- Handling Slowly Changing Dimensions (SCD Types 1, 2, and 3)
- Optimizing Schemas for Fast Querying and Aggregation
- Implementing effective surrogate keys and natural keys
- Data Normalization and De-Normalization Trade-offs
UNIT 3: Data Ingestion and Transformation
Core ETL/ELT Techniques- Data Extraction Methods: APIs, Databases, Files, and Streaming
- Data Cleansing and Standardization Techniques using Python/SQL
- Data Enrichment and Feature Engineering basics
- Implementing Incremental Loads vs. Full Loads
- Handling data type conversions and null values gracefully
UNIT 4: Pipeline Orchestration and Automation
Building Robust Data Workflows- Introduction to Workflow Management Systems (WMS) like Apache Airflow
- Defining Directed Acyclic Graphs (DAGs) and Tasks
- Scheduling, Monitoring, and Retries in Data Pipelines
- Managing Dependencies and ensuring Idempotency in tasks
- Implementing version control (Git) for data pipeline code
- Understanding infrastructure-as-code (IaC) principles for pipelines
UNIT 5: Data Quality and Governance
Ensuring Trustworthy Data- Establishing Data Quality Frameworks and Metrics
- Implementing checks for completeness, validity, and consistency
- Automating Alerting and Error Handling in Pipelines
- Introduction to Metadata Management and Data Lineage Tracking
- Best Practices for Data Security and Compliance within the pipeline flow
- Documenting Data Dictionaries and Pipeline Logic
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