Course Overview

In today’s digital economy, organisations generate enormous volumes of data from diverse sources, creating both opportunities and challenges for businesses seeking to gain meaningful insights. Transforming raw, unstructured data into reliable, accessible, and actionable information requires robust data engineering practices, scalable architectures, and efficient data processing technologies.

The Advanced Data Engineering Programme by Transformentors Academy provides participants with comprehensive knowledge and practical skills in designing, building, and managing modern data ecosystems. The programme explores database design, data modelling, data integration, pipeline automation, data warehousing, and large-scale data processing using industry-leading tools and technologies.

Through hands-on exercises, real-world case studies, and practical simulations, participants will learn how to develop scalable data infrastructures, automate data workflows, and process high-volume datasets efficiently. The programme also covers modern data engineering frameworks and platforms, including Python, SQL, Apache NiFi, Apache Spark, and Apache Kafka.

By the end of the programme, participants will be equipped to design and implement end-to-end data solutions that support analytics, business intelligence, machine learning, and data-driven decision-making across their organisations.

Agenda

Day — 1 Programming Foundations for Data Engineering

• Understanding the role and lifecycle of data engineering within modern data ecosystems.
• Exploring real-world applications of data engineering across various industries.
• Developing Python programming skills for data processing and automation.
• Utilizing Python libraries such as NumPy and Pandas for data manipulation and analysis.
• Applying version control principles using GitHub for collaborative development.
• Understanding software engineering fundamentals, including functions, modules, and development environments.
• Building a foundation for developing data-driven and web-based applications using advanced Python concepts.

Day — 2 Databases and SQL

• Understanding the fundamentals of databases, SQL, and their role in data engineering.
• Applying core SQL operations for data retrieval and manipulation.
• Working with SQL statements such as SELECT, INSERT, UPDATE, and DELETE.
• Utilizing advanced SQL techniques, including joins, aggregations, and subqueries.
• Performing database analysis to support data management and reporting requirements.
• Understanding client–server database interactions and communication principles.
• Exploring the fundamentals of client–server architecture in database environments.
• Comparing relational and non-relational database technologies, including PostgreSQL, MySQL, and NoSQL databases.
• Understanding database deployment and containerisation concepts for modern data platforms.
• Exercise: Writing SQL queries to analyse a relational dataset using JOINs and other advanced SQL techniques.

Day — 3 ETL and Data Pipelines

• Understanding the principles and architecture of ETL (Extract, Transform, Load) processes.
• Exploring the stages of data extraction, transformation, and loading within modern data ecosystems.
• Building and managing ETL workflows using Apache NiFi.
• Designing scalable and efficient data pipelines to support business and analytics requirements.
• Understanding the concepts and importance of Change Data Capture (CDC) for real-time data integration.
• Implementing real-time data ingestion pipelines using Debezium and CDC technologies.
• Exploring the role of Java-based tools and frameworks in data pipeline development.
• Integrating data from APIs and external sources into data engineering workflows.
• Understanding data movement, transformation, and processing across distributed systems.
• Exercise: Building and testing a simple ETL pipeline using Apache NiFi.

Day — 4 Big Data Tools and Orchestration

• Understanding the architecture and platforms used for big data processing and analytics.
• Processing large-scale datasets using Apache Spark and distributed computing techniques.
• Exploring the fundamentals of workflow orchestration with Apache Airflow.
• Designing and managing automated data workflows using Directed Acyclic Graphs (DAGs).
• Understanding batch processing and stream processing methodologies for big data environments.
• Utilizing tools and platforms such as Apache Kafka, Mosquitto, and ThingsBoard for data streaming and IoT data management.
• Implementing real-time data processing and event-driven data architectures.
• Monitoring, logging, and troubleshooting data pipelines to ensure reliability and performance.
• Applying best practices for scalable, resilient, and efficient data engineering workflows.
• Exercise: Creating a basic Apache Airflow DAG to simulate an ETL pipeline using sample data.

Day — 5 Advanced Applications and Machine Learning

• Applying regression techniques in Python for predictive analysis and data modelling.
• Understanding the fundamentals of machine learning and probability concepts used in data science.
• Exploring advanced machine learning topics, including reinforcement learning and deep neural networks.
• Integrating ETL processes, data analysis, and visualisation within end-to-end data projects.
• Utilizing data-driven insights to support business strategy and decision-making.
• Applying machine learning workflows using Python libraries such as Pandas and Scikit-Learn.
• Creating visualisations that effectively communicate analytical findings and model outcomes.
• Exercise: Building, evaluating, and visualising a machine learning model using Pandas and Scikit-Learn.
• Course evaluation, key takeaways, and programme recap.

Learning Outcomes

By the end of this programme, participants will be able to:

• Apply Python programming techniques for data engineering and automation tasks.
• Design and manage relational databases using SQL and modern database architectures.
• Build and automate ETL pipelines for efficient data integration and processing.
• Implement real-time data ingestion and streaming solutions using industry-standard tools.
• Process and analyse large-scale datasets using distributed data processing frameworks.
• Design, schedule, monitor, and maintain reliable data workflows and pipelines.
• Integrate IoT data sources into data engineering ecosystems.
• Apply data visualisation and basic machine learning techniques to generate business insights.
• Develop scalable and efficient data infrastructures that support analytics and decision-making.

Who Should Attend

This programme is ideal for:

• Data Engineers and Data Analysts.
• Software Developers and Database Administrators.
• IT Infrastructure, Systems, and Cloud Engineers.
• Business Intelligence (BI) and Analytics Professionals.
• Machine Learning and Artificial Intelligence Practitioners.
• Data Architects and Data Platform Specialists.
• Technology Professionals involved in data integration, automation, and analytics.
• Professionals seeking to advance their careers in big data, data engineering, and modern data infrastructure.