course | Data Engineering with ETL/ELT Pipelines
Join the Data Engineerin... course taught in English
DIGTR-3177 | Data Engineering with ETL/ELT Pipelines
Course Sector : Digital Transformation and Innovation
| Duration | Date from | Date to | Course Venue | Course fees | Book a course |
|---|---|---|---|---|---|
| 5 Days | 30/08/2026 | 03/09/2026 | Cairo | $4,250 | Book now |
| 5 Days | 11/10/2026 | 15/10/2026 | Online | $2,150 | Book now |
| 5 Days | 05/04/2027 | 09/04/2027 | Dubai | $4,250 | Book now |
| 5 Days | 13/06/2027 | 17/06/2027 | Riyadh | $4,250 | Book now |
Course Introduction
ETL and ELT pipelines are the backbone of modern data architecture, enabling organizations to efficiently move, transform, and store data for analytics and business intelligence. This program equips participants with intermediate-level skills to design, develop, and optimize ETL/ELT workflows using SQL, Python, and orchestration tools.
Participants will gain hands-on knowledge of data ingestion, transformation, and pipeline automation, preparing them to manage complex datasets and integrate multiple sources efficiently.
Course objective
- Design and implement scalable ETL/ELT pipelines.
- Perform advanced data transformations and modeling using SQL and Python.
- Optimize pipeline performance for large datasets.
- Schedule, monitor, and orchestrate data workflows.
- Ensure data quality, integrity, and governance in pipeline
Course audience
- Data Engineers
- Database Administrators (DBAs)
- Data Analysts and BI Professionals
- Software Developers
Course Outline | DAY 01
Fundamentals of ETL/ELT Development
- ETL vs ELT concepts and best practices
- Data ingestion from multiple sources
- Data transformation techniques
- Introduction to data quality and validation
- Overview of ETL/ELT tools and frameworks
Course Outline | DAY 02
Advanced SQL for Data Transformation
- Complex joins, window functions, and CTEs
- Aggregations, subqueries, and stored procedures
- Data modeling for analytics
- Performance tuning of SQL queries
- Using SQL for pipeline transformations
Course Outline | DAY 03
Python for Data Engineering
- Data extraction, cleaning, and transformation with Python
- Pandas for structured data manipulation
- Integrating Python scripts into ETL workflows
- Error handling and logging
- Python best practices for pipeline development
Course Outline | DAY 04
Introduction to Apache Spark & PySpark
- Spark architecture and RDD/DataFrame concepts
- PySpark for ETL and data transformation
- Spark SQL and DataFrame operations
- Optimizing Spark jobs for performance
- Partitioning and caching strategies
Course Outline | DAY 05
Data Orchestration and Pipeline Automation
- Scheduling pipelines with Airflow/Prefect
- Dependency management and monitoring
- Error handling and alerting
- Pipeline versioning and reproducibility
- Best practices for production-grade data workflows
BOOST’s Professional Attendance Certificate “BPAC”
BPAC is always given to the delegates after completing the training course,and depends on their attendance of the program at a rate of no less than 80%,besides their active participation and engagement during the program sessions.
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