Introduction

In the modern data ecosystem, the ability to build and manage robust data pipelines is a foundational skill that transforms raw data into a reliable source of truth, making ETL and ELT Development with SQL and Python an essential discipline for all data professionals. This training course is designed to provide a comprehensive, hands-on experience in the critical processes of Extracting, Transforming, and Loading (ETL) data, with a special focus on the modern Extract, Load, and Transform (ELT) approach that has been popularized by cloud data warehouses. By mastering both SQL for complex data manipulation and Python for scripting, automation, and integration, participants will be empowered to design, build, and maintain efficient, scalable, and resilient data pipelines that feed business intelligence dashboards, machine learning models, and strategic decision-making, thereby enabling organizations to harness the full value of their data assets.

Duration

10 days

Target Audience

• Data Engineers
• Data Analysts
• BI Developers
• Aspiring ETL/ELT Developers
• Database Developers
• Data Scientists
• Professionals working with large datasets
• IT professionals involved in data migration
• Anyone interested in building data pipelines
• Students in a data-related field

Objectives

• Understand the core concepts of ETL and ELT and when to use each.
• Master SQL for complex data transformation and manipulation.
• Develop proficiency in using Python for data extraction and scripting.
• Learn to build complete, end-to-end data pipelines using both SQL and Python.
• Understand different data sources and how to extract data from them.
• Explore strategies for data cleansing, validation, and quality control.
• Learn about data loading techniques for various databases and data warehouses.
• Develop skills in building modular, reusable, and maintainable ETL/ELT code.
• Understand the importance of error handling, logging, and monitoring in pipelines.
• Formulate a strategic approach to designing and documenting data workflows.

Course Content

Module 1. Introduction to Data Pipelines

• The importance of data pipelines in modern business
• The data lifecycle: from source to insight
• Defining ETL (Extract, Transform, Load): The traditional approach
• Defining ELT (Extract, Load, Transform): The modern approach
• Key differences and the paradigm shift

Module 2. SQL for Data Transformation

• Advanced SQL Concepts: Joins, subqueries, and window functions
• Using CTEs (Common Table Expressions) for readability and modularity
• Data transformation functions: CASE statements, COALESCE, and string manipulation
• Aggregation and group functions for summary statistics
• Best practices for writing clean, optimized, and maintainable SQL

Module 3. The Extract Process with Python

• Python for Data Extraction: Why it's the go-to language
• Connecting to Databases: Using sqlalchemy and psycopg2
• Calling APIs: Using the requests library to fetch data
• Reading data from files: CSV, JSON, Parquet
• Writing a simple, reusable extraction function

Module 4. The Transform Process with Python and Pandas

• Introduction to Pandas: The fundamental library for data manipulation
• DataFrames: The primary data structure for transformation
• Data cleansing techniques: handling null values, duplicates, and outliers
• Data shaping: Merging, joining, and pivoting data
• Creating new features and aggregations

Module 5. The Load Process

• Loading Data into Databases: Using Python with sqlalchemy
• Bulk Loading Techniques: The COPY command, pandas to_sql with fast_executemany
• Choosing between INSERT, UPDATE, and MERGE (upsert)
• Loading data into a cloud data warehouse (conceptual overview)
• Error handling during the loading phase

Module 6. Building an ETL Pipeline

• End-to-End Project: From an API to a relational database
• Step 1: Extract: Fetching data with Python
• Step 2: Transform: Cleaning and structuring the data with Pandas
• Step 3: Load: Inserting the data into a database table
• Orchestrating the three steps into a single script
• Simple logging and monitoring

Module 7. Building an ELT Pipeline

• The ELT workflow: Extract, Load, then Transform
• Extract and Load: Moving raw data directly to a data warehouse
• Transform: Using SQL on the data warehouse
• The benefits of using the data warehouse's compute power
• Practical example with a mock cloud data warehouse

Module 8. Data Quality and Validation

• The importance of data quality in pipelines
• Implementing Data Validation Checks: Schema validation, data type checks
• Handling errors and data rejections
• Strategies for continuous data quality monitoring
• Best practices for data governance in your pipelines

Module 9. Incremental vs. Full Loads

• Full Load: When to refresh all data
• Incremental Load: When to only process new or changed data
• Strategies for identifying and tracking new data
• Using watermarks and timestamps for incremental updates
• The trade-offs: Simplicity vs. efficiency

Module 10. Data Orchestration and Workflow Management

• The need for an orchestration tool
• Introduction to Apache Airflow (conceptual)
• Defining a DAG (Directed Acyclic Graph) for your pipeline
• Scheduling and monitoring tasks
• Using a simple workflow manager to run your scripts

Module 11. Real-World Case Study: ETL from a Flat File

• Project Overview: Processing a CSV file from a server
• Extract: Downloading the file and reading it with Python
• Transform: A more complex transformation scenario with multiple steps
• Load: Updating a database with the processed data
• Handling file archives and history
• Documenting the pipeline

Module 12. Real-World Case Study: ELT for a Cloud Warehouse

• Project Overview: A modern ELT pipeline
• Extract: Getting data from a source (e.g., API)
• Load: Staging the data in a cloud object storage (conceptual)
• Transform: Using SQL to transform the data within the cloud data warehouse
• Using a tool like dbt (conceptual) for SQL-based transformations
• Finalizing the data for a BI dashboard

Module 13. Error Handling, Logging, and Monitoring

• Robust Error Handling: Using try/except blocks
• Logging: The importance of logging in pipelines
• Best practices for logging: timestamps, levels, context
• Monitoring pipeline health: Status checks and alerts
• Strategies for debugging pipeline failures

Module 14. Performance and Optimization

• Database Performance: Indexing and query optimization
• Optimizing Python code for speed
• Vectorization with Pandas for faster operations
• Understanding parallel processing and concurrency
• Choosing the right tools for the job

Module 15. The Future of ETL/ELT

• Cloud-Native Solutions: Managed services for data pipelines
• The rise of dbt and SQL-based transformations
• Streaming Data: Spark Streaming, Apache Kafka, Flink
• The emergence of Data Mesh architectures
• The evolving role of the data engineer.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.