Data Engineering for Manufacturing

Duration: 2 days

This comprehensive training programme equips engineering, data, and IT teams with the skills to design and implement robust data pipelines for AI and analytics use cases in manufacturing.

Beyond manufacturing, these same data engineering skills also power AI-driven search, Answer Engine Optimisation (AEO), and Generative Engine Optimisation (GEO), where clean, structured, and well-orchestrated data is essential for AI models to deliver accurate and trusted outputs.

Participants will learn to collect, clean, store, and orchestrate large volumes of machine and sensor data using modern Python-based tools.

The course emphasises real-time data flow, ETL processes, and handling both structured and unstructured data, preparing participants to support machine learning models and business reporting with high-quality datasets. Practical exercises using real manufacturing data will reinforce each concept, ensuring participants can apply their skills to real-world scenarios.

By the end of this programme, you will learn to:

Understand the data lifecycle in manufacturing—from ingestion to storage and analysis.
Design ETL (Extract, Transform, Load) pipelines for both real-time and batch processing.
Transform data using Python libraries such as pandas, PySpark, and SQLAlchemy.
Connect to databases, cloud storage, and APIs to manage industrial data flows.
Apply principles of data quality, normalisation, and validation.
Implement basic concepts of pipeline orchestration and monitoring.
Prepare and engineer datasets for machine learning and reporting needs.

Programme Outline

Module 1: Introduction to Data Engineering in Manufacturing

What is data engineering, and how is it different from data science?
Understanding manufacturing data: machine logs, IoT sensor feeds, QA reports.
Data pipeline architecture: ingestion, storage, processing, consumption.
Use Case: Mapping sensor data flow from equipment to dashboards.

Module 2: Data Ingestion Techniques

Working with CSVs, JSON, SQL, APIs, MQTT, and IoT data streams.
Reading data from production equipment exports, shopfloor systems, and databases.
Hands-on: Load and inspect raw data from manufacturing operations.
Use Case: Pulling live sensor data into a processing pipeline.

Module 3: Data Cleaning & Transformation

Handling missing data, time-based gaps, and inconsistent formats.
High-level explanation: Normalisation, denormalisation, data types, and schemas.
Introduction to PySpark for large-scale data transformations.
Hands-on: Clean and transform defect logs and cycle time reports.
Use Case: Reshaping raw sensor logs into structured ML-ready tables.

Module 4: Storing & Managing Data

Introduction to relational (PostgreSQL) and NoSQL (MongoDB) databases.
Understanding schema design for manufacturing applications.
High-level insight: Relational models vs document-based models.
Hands-on: Create tables and insert processed data into SQL databases.
Use Case: Centralising QA and sensor data for analytics teams.

Module 5: Building and Automating ETL Pipelines

What is ETL and why is it essential for manufacturing analytics?
Tools: Airflow, Prefect (conceptual intro), and Python scripts.
Hands-on: Build a mini-ETL pipeline to extract, clean, and store data.
Use Case: Automating daily ingestion and preprocessing of shift reports.

Module 6: Data Quality, Validation & Monitoring

Key concepts: data integrity, duplication, null checks, schema drift.
Techniques: logging, exception handling, alerting.
Hands-on: Write validation scripts for incoming sensor data.
Use Case: Monitoring incoming data for critical faults or unexpected values.

Module 7: Engineering Datasets for ML & Dashboards

Structuring data for machine learning: time series windowing, label creation.
Preparing data for Power BI/Tableau dashboards.
Hands-on: Build and export a feature store for an ML model.
Use Case: Supplying clean data for a yield prediction ML model.
Discuss how engineered datasets also form the backbone of AI-driven discovery and optimisation, ensuring content and predictions remain accurate in contexts such as AEO and GEO.

Q&A and Wrap-Up

Training Methodology

This workshop combines expert-led lectures, hands-on labs, and real manufacturing datasets to ensure participants gain practical, industry-relevant skills. Activities include pipeline design challenges, group discussions, and architectural visualisations to reinforce learning.

The methodology also draws connections to AI, AEO, and GEO, showing participants how strong data foundations in manufacturing translate into wider AI optimisation strategies.

Who Should Attend

This programme is ideal for data engineers and IT teams, manufacturing process engineers, automation and system integrators, as well as data scientists supporting production analytics.