Enterprise systems generate vast volumes of transactions, but the information inside them is often locked in complicated schemas, duplicated across modules and inconsistent between departments. Meanwhile, businesses want timely reports, analytics and integration with new cloud tools. Professionals who can bridge databases, ERP structures and modern data engineering practice are therefore in steady demand across banks, manufacturers, public bodies and NGOs.

This ten-day programme gives participants a complete, practical route from database fundamentals to production-style pipelines. It begins with relational theory and SQL, since reliable engineering depends on fluent querying and sound design. The course then turns to ERP data: how core tables are organised, how master and transactional data differ, why migrations fail and how to plan extraction, mapping, cleansing and reconciliation. Data engineering practice follows, with ETL and ELT patterns, scripting, scheduling, logging, error handling, quality checks and documentation.

The closing sessions focus on analytical delivery. Participants design dimensional models and warehouses, load them from ERP extracts, expose results through reports, and consider security, backup, governance and monitoring. Each day is organised around lab exercises on supplied datasets, and the programme ends with an integrated project in which each team delivers a small end-to-end data platform.

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

• Design normalised relational schemas and document them with entity relationship diagrams.
• Write SQL queries using joins, subqueries, aggregation, window functions and views.
• Tune queries with indexes, execution plans and sound transaction handling.
• Describe how ERP modules store finance, procurement, inventory, sales and HR data.
• Plan and execute an ERP data extraction, mapping, cleansing and reconciliation exercise.
• Build ETL or ELT pipelines using SQL and Python with logging and error handling.
• Create dimensional models and star schemas and load them into a data warehouse.
• Apply data quality checks, security controls and API integration to data platforms.

The course is practical in focus and suits those who handle enterprise data, including:

• Database administrators and database developers
• ERP functional consultants and system analysts
• Data engineers and aspiring data engineers
• Business intelligence developers and report writers
• IT officers responsible for ERP support and migration projects
• Systems and IT auditors
• Finance, supply chain and HR data owners who extract ERP data
• Software developers integrating applications with enterprise systems

Participants complete the programme with:

• A normalised database design and a library of tested SQL queries.
• A migration plan and mapping template for moving ERP data.
• A working ETL pipeline with scheduling, logging and validation steps.
• A star schema warehouse loaded from ERP-style source data.
• A data quality checklist and reconciliation approach.
• A completed capstone project they can describe in their own portfolio.

The programme is lab driven so that every concept is practised on working data:

• Short instructor demonstrations followed by guided hands-on labs
• Practice databases and ERP-style datasets supplied for each exercise
• Pair programming and peer code review during pipeline development
• Troubleshooting sessions built on realistic migration and load failures
• A final team capstone presented to the facilitators

Participants who attend the ten days and complete the labs and capstone are granted a CPD-accredited Certificate of Completion by Vision Reach Global Consultancy.