Modern organisations generate data in dozens of systems, with the ERP at the centre holding finance, procurement, inventory, sales and human resources records. Getting that data into a form that analysts and leaders can trust requires engineering, not just exporting files. Manual extracts break, definitions drift between departments and reports disagree, which erodes confidence in the numbers.

This practical programme runs for ten days and builds data engineering skills from the ground up. Participants start with relational database design and structured query language, working through normalisation, keys and constraints, complex queries, window functions, views, stored procedures and performance tuning. They then learn Python for data work, using pandas to read, reshape, validate and combine data from files, databases and web services. The middle of the course introduces data architecture, including staging areas, data warehouses and lakes, dimensional modelling, and the difference between ETL and ELT.

The second half concentrates on ERP data. Participants study typical ERP table structures and master and transactional data, extract data through database connections, APIs and reports, and resolve problems such as inconsistent codes, duplicates and history. They build scheduled pipelines, implement quality checks and logging, apply security and access controls, and publish curated datasets for analytics. The programme closes with a capstone project in which each participant delivers a documented pipeline and warehouse for a realistic business case.

By the end of the course, participants will be able to:

• Design normalised relational schemas and document them with entity relationship diagrams.
• Write advanced SQL queries, views and procedures and tune their performance.
• Use Python and pandas to clean, transform and combine data from multiple sources.
• Build and schedule ETL and ELT pipelines with logging and error handling.
• Model a dimensional data warehouse using fact and dimension tables.
• Extract and interpret master and transactional data from ERP systems.
• Implement data quality rules, reconciliation checks and access controls.
• Publish curated datasets to Power BI or similar reporting tools.

The course is designed for technical and semi-technical professionals who work with organisational data, including:

• Data analysts and business intelligence developers
• Database administrators and systems administrators
• ERP super users, functional consultants and finance systems officers
• Software developers moving into data roles
• Statisticians and M&E data officers in NGOs and government
• ICT officers in banks, utilities, telecoms and public institutions
• Internal auditors who analyse ERP data
• Graduates entering data engineering careers

Participants leave the course with:

• A normalised database design with a documented data dictionary.
• A library of tested SQL queries and reusable Python scripts.
• A working scheduled pipeline from source system to warehouse.
• A star schema warehouse built from sample ERP data.
• Data quality and reconciliation checks they can reuse.
• A finished capstone project they can show to employers or managers.

The course is more than three-quarters hands-on, with every concept followed by a lab. Participants will experience:

• Daily computer labs using PostgreSQL or SQL Server, Python, Jupyter and Power BI
• Sample ERP databases with realistic finance, procurement and inventory data
• Code walk-throughs and pair programming with trainer feedback
• Short architecture briefings that connect each skill to the whole data flow
• A ten-day capstone that grows from the labs into a documented pipeline

Participants who attend the ten days, complete the daily labs and submit the capstone project receive a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.