Most organisations now run an ERP or several line-of-business systems, and each holds valuable data locked in complex schemas. Turning that data into dependable analytics requires more than reporting skills. It needs sound database design, repeatable pipelines, data quality controls and an understanding of how business transactions are stored in ERP modules.

This professional programme runs across ten days and progresses from foundations to integrated delivery. Participants first strengthen their command of relational modelling and SQL, including window functions, indexing strategy, performance tuning, transaction handling and role-based security. They then learn data engineering practice: designing ETL and ELT jobs in Python and SQL, scheduling and monitoring, handling incremental loads and schema changes, and testing for quality. A substantial block addresses ERP data, from understanding table structures and master data to extraction, migration cut-over planning and reconciliation to the general ledger.

The closing days bring everything together by modelling a star schema, loading a warehouse from a sample ERP extract and exposing results through dashboards. Work is almost entirely lab based on a provided environment. Delegates complete a capstone project and leave with code, diagrams and runbooks reusable on real systems.

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

• Design normalised relational schemas with appropriate keys, constraints and indexes.
• Write and tune advanced SQL, including joins, window functions and common table expressions.
• Build ETL and ELT pipelines in Python and SQL with logging, error handling and incremental loads.
• Schedule, orchestrate and monitor data workflows.
• Interpret the data structures behind core ERP modules and extract data safely.
• Plan and execute data migration with cleansing, mapping and reconciliation.
• Model dimensional warehouses and deliver reporting layers for business intelligence tools.
• Apply testing, documentation, security and access controls to data platforms.

The course is intended for technical professionals who already work with data or business systems, including:

• Database administrators and developers
• Data engineers and analytics engineers
• ERP functional and technical consultants
• Business intelligence and reporting developers
• Systems analysts and integration specialists
• ICT managers responsible for enterprise applications
• Finance and operations analysts who own system data
• Internal auditors performing data-driven reviews

Participants leave with:

• A normalised database design and entity relationship diagram built from a business case.
• A library of tuned SQL patterns for reporting and reconciliation.
• A working Python ETL project with scheduling, logging and tests.
• An ERP data extraction and mapping guide for finance and supply modules.
• A migration plan with cleansing rules and reconciliation checks.
• A star schema warehouse and dashboard from their capstone project.

This course is overwhelmingly practical, and every concept is applied in a provided lab environment. Delegates will:

• Follow short demonstrations, then repeat each technique themselves
• Work on a sample ERP database seeded with realistic transactions and defects
• Develop a version-controlled pipeline project over the ten days
• Take part in code reviews and troubleshooting clinics
• Present a capstone solution and defend design choices

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