29/12/2025
Module I: Data Engineering Basics for Everyone
Learning Objectives
• Discuss overview of the modern data ecosystem
• Identify key players in the data ecosystem
• Recognise introduction to Kubernetes objects
• Analyse defining data analysis
• Identify data analytics vs data analysis
• Recognise responsibilities of a data analyst
• Identify a day in the life of a data analyst
• Analyse understanding different types of data
• Analyse sources of data using service bindings
• Learn overview of RDBMS and NoSQL databases
• Analyse introduction to data marts and data lakes
• Recognise ETL and data pipelines
• Analyse foundations of big data and processing tools
• Analyse identifying data for analysis
• Recognise data sources and collection methods
• Analyse tools of data wrangling
• Identify data cleaning techniques
• Analyse overview of statistical analysis
• Learn introduction to data mining and data visualisation
• Identify career paths in data analysis
Introduction
A data ecosystem is a group of business infrastructure and applications used to collect, analyse, store, and manage data. It helps organisations understand customers better and make informed decisions for marketing, sales, and operational efficiency.
1.1 The Modern Data Ecosystem
A modern data ecosystem, often referred to as a technology stack, includes responsive data design, scalable delivery systems, and AI-driven data management. It consists of tools, programming languages, cloud services, and frameworks used to collect, store, analyse, and visualise data.
Key Components of a Data Ecosystem
1. Data Sensing – Identifying useful data sources
2. Data Collection – Gathering data from internal and external sources
3. Data Wrangling – Cleaning and preparing raw data
4. Data Storage – Using databases and cloud systems
5. Data Analysis – Applying statistical and analytical methods
6. Data Visualisation – Representing insights using charts and dashboards
A person who is responsible for building and managing the data architecture is called
a data engineer. He or she is responsible for making the data accessible for use in the
operations and analysis of the company. An online transaction process, also known
As the real database is referred to as OLTP. A data engineer is required to distinguish
between OLTP and the maintenance of a data warehouse or OLAP, which is essentially
a duplicate of the database used for analysis. Any queries that need to be executed have
to be implemented on an OLAP system rather than an OLTP system. It was necessary
to exert the effort required to acquire the data from a variety of sources. After this they
will be tasked with organising and reorganising the data. It is necessary to construct data
repositories in order to store the data.
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