Data Analytics & Business Intelligence · Physical, Online
Modern Big Data Analytics, Data Engineering & AI
10 Days2 sessions available Certificate on completion
**Big Data Analytics & Data Engineering Technologies** is a practical professional course designed to equip participants with an understanding of how modern organizations collect, integrate, store, process, manage and analyze large and complex datasets. The course explores the complete data engineering and analytics lifecycle, including data ingestion, data storage, distributed processing, ETL/ELT, data pipelines, data lakes, data warehouses, NoSQL databases, real-time data processing, cloud data platforms and large-scale analytics. Participants are introduced to technologies such as **Python, SQL, Hadoop, Apache Spark, PySpark and Kafka**, with practical examples demonstrating how these technologies support scalable data platforms, business intelligence and data-driven decision-making.
**Big Data Analytics & Data Engineering Technologies** introduces participants to modern technologies and practices for managing, processing and analyzing large volumes of data. The course covers key areas such as data pipelines, distributed processing, data lakes, data warehouses, cloud platforms, Hadoop, Apache Spark, Python and SQL, helping professionals develop practical skills for building scalable and data-driven solutions.
Course objectives
Understand Big Data and Modern Data Engineering Architectures
Collect, Integrate and Engineer Data from Multiple Sources
Apply Big Data Storage and Processing Technologies
Develop Scalable Data Analytics and Engineering Solutions
Design Secure, Governed and End-to-End Big Data Solutions
Who should attend
Data Analysts, Data Engineers, Data Scientists, Database Administrators, Software Developers, Business Intelligence Professionals, GIS Professionals, ICT Professionals, Systems Analysts, Statisticians, Researchers, Data Management Professionals, Monitoring and Evaluation Professionals, IT Officers, Cloud Professionals, Machine Learning Practitioners, AI Professionals, University Students, College Students, Computing Students, ICT Graduates, Statistics Graduates, Business Analytics Professionals, Financial Analysts, Government Data Professionals, Banking Professionals, Energy Professionals, Petroleum Professionals, Environmental Data Professionals, Transport Data Professionals, Organisations Transitioning to Cloud Data Platforms, Professionals Transitioning into Data Engineering, Professionals Transitioning into Data Analytics, Professionals Seeking Big Data and AI Skills.
Course content
Module 1: Introduction to Environmental Sustainability and Sustainable Development This module establishes the foundations of environmental sustainability and sustainable development. Participants explore the meaning of sustainability and examine the rel
Module 2: Principles and Dimensions of Sustainable Development Participants examine the three interconnected dimensions of sustainable development: environmental, economic and social sustainability. The module explores how development decisions can creat
Module 3: Sustainable Development Goals and Global Sustainability Agenda This module introduces the UN Sustainable Development Goals (SDGs) and their relevance to organizations, governments, development programmes and communities. Participants explore th
Module 4: Environmental Challenges and Sustainability Risks Participants examine major environmental challenges affecting sustainable development, including pollution, land degradation, deforestation, biodiversity loss, water scarcity, waste generation,
Module 5: Climate Change, Resilience and Sustainable Development This module examines the relationship between climate change and sustainable development. Participants explore greenhouse gas emissions, climate risks, vulnerability, adaptation, mitigation
Module 6: Sustainable Resource Management and Resource Efficiency Participants learn how natural resources can be managed responsibly to support long-term development. The module covers efficient use of water, energy, land, raw materials and other resour
Module 7: Circular Economy and Sustainable Production and Consumption This module introduces the circular economy as an alternative to traditional linear patterns of production and consumption. Participants explore concepts such as reduce, reuse, repair,
Module 8: Renewable Energy, Energy Efficiency and Low-Carbon Development Participants examine the role of energy in sustainable development and explore renewable energy and energy-efficiency approaches. The module introduces solar, wind, hydro and other
Module 9: Biodiversity, Ecosystems and Sustainable Natural Resource Use This module focuses on biodiversity and ecosystems as foundations of sustainable development. Participants examine ecosystem services, habitat loss, land-use change, conservation and
Module 10: Sustainable Cities, Communities and Infrastructure Participants explore sustainability challenges associated with urbanization and population growth. Topics include sustainable land use, public transportation, waste management, water systems,
Module 11: Sustainable Agriculture, Food Systems and Land Management This module examines sustainability within agriculture and food systems. Participants explore sustainable farming, soil conservation, water management, climate-smart agriculture, biodiv
Module 12: Green Economy, Green Jobs and Sustainable Business Participants examine the concept of the green economy and its implications for businesses, governments and development organizations. The module explores sustainable business models, green pro
Module 13: ESG, Sustainability Strategy and Organizational Performance This module introduces Environmental, Social and Governance (ESG) concepts and their growing relevance to organizational sustainability. Participants examine environmental performance
Module 14: Sustainability Indicators, Monitoring and Reporting Participants learn how sustainability performance can be measured and communicated. The module covers sustainability indicators, environmental performance metrics, baseline assessment, target
Module 15: Integrated Sustainable Development and Sustainability Action Project The final module brings together the concepts covered throughout the programme. Participants work on a practical organization, community or development scenario and identify
General notes
**Big Data Analytics & Data Engineering Technologies** is a comprehensive professional course that introduces participants to the technologies, architectures and practices used to manage, process and analyze large and complex datasets. The course provides an overview of the modern data engineering lifecycle, covering data sources and ingestion, data storage, data processing, ETL/ELT, data pipelines, distributed computing, data lakes, data warehouses, NoSQL databases, real-time data processing, cloud platforms and big data analytics. Participants gain an understanding of how technologies such as **Python, SQL, Hadoop, Apache Spark, PySpark, Kafka and cloud data platforms** work together to support scalable data solutions. The course is suitable for professionals seeking to understand, implement or work with modern enterprise data platforms and large-scale analytics environments.
Frequently asked questions
Yes. The content can be customized around an organization's data infrastructure, industry, existing technologies, reporting requirements, data architecture and specific business use cases.
Yes. Participants are introduced to streaming data, event processing and technologies such as Kafka, with practical examples of real-time data applications.
Hadoop provides a foundation for distributed storage and processing of large datasets. The course explains the Hadoop ecosystem and how it relates to modern Big Data architectures.