Big Data Technology: Tools and Technologies for Business Analytics

Asst. Prof. Dr. Somsak Chanaim

International College of Digital Innovation, CMU

July 10, 2026

Learning Objectives

After this lesson, students will be able to:

  • Explain the meaning and purpose of Big Data Technology
  • Describe why Big Data requires specific technologies
  • Understand the Big Data technology pipeline
  • Identify suitable tools for storage, processing, analytics, and visualisation
  • Select appropriate Big Data tools for business problems

1. Definition of Big Data Technology

Big Data Technology refers to the tools, platforms, and systems used to collect, store, process, analyse, and visualise large and complex datasets.

In business analytics, Big Data Technology helps organisations transform raw data into useful insights for better decision-making.

Role in Business Analytics

Big Data Technology supports business analytics by helping organisations:

  • Understand customer behaviour
  • Improve operational efficiency
  • Detect trends and patterns
  • Forecast future demand
  • Support data-driven decisions

2. Why Big Data Needs Technology

Big Data cannot be managed effectively using only traditional tools because it involves:

  • Volume: very large amounts of data
  • Variety: different data formats
  • Velocity: data generated very quickly
  • Complexity: data from many systems and sources
  • Scalability: systems must grow with data size

Example: Traditional Data vs Big Data

Traditional Data Big Data
Small to medium size Very large size
Structured tables Structured, semi-structured, unstructured
Stored in RDBMS Stored in cloud, NoSQL, HDFS
Processed on one server Processed using distributed systems
Basic reports Advanced analytics and AI

3. Big Data Technology Pipeline

A Big Data pipeline is a sequence of steps used to turn raw data into business insights.

flowchart LR
A[Data Collection] --> B[Data Storage]
B --> C[Data Processing]
C --> D[Data Analytics]
D --> E[Data Visualisation]
E --> F[Decision-Making]

Step 1: Data Collection

Data can be collected from many sources, such as:

  • Sales transactions
  • Customer feedback
  • Website clicks
  • Social media
  • Mobile applications
  • IoT sensors
  • Business documents

Step 2: Data Storage

After collection, data must be stored in systems that can handle large size and different formats.

Common storage technologies include:

  • RDBMS
  • NoSQL databases
  • Cloud storage
  • Hadoop HDFS

4. Data Storage Technologies

RDBMS

Relational Database Management Systems store data in structured tables.

Examples:

  • MySQL
  • PostgreSQL
  • Microsoft SQL Server
  • Oracle Database

Best for structured business data such as customer records, sales, and inventory.

NoSQL Databases

NoSQL databases are designed for flexible and scalable data storage.

They are suitable for:

  • Semi-structured data
  • Large-scale applications
  • Real-time systems
  • Web and mobile data
  • MongoDB
  • Cassandra
  • Redis
  • Firebase

Cloud Storage

Cloud storage allows organisations to store data online and scale storage as needed.

  • Google Cloud Storage
  • Amazon S3
  • Microsoft Azure Blob Storage
  • Flexible storage capacity
  • Easy access
  • Lower infrastructure cost
  • Suitable for backup and analytics

Hadoop HDFS

Hadoop Distributed File System, or HDFS, stores very large datasets across multiple computers.

It is useful when:

https://hadoop.apache.org/

https://hadoop.apache.org/

  • Data is too large for one machine

  • Distributed storage is required

  • Batch processing is needed

5. Data Processing Technologies

Big Data processing technologies are used to clean, transform, and analyse large datasets.

Common technologies include:

  • Hadoop MapReduce
  • YARN
  • Apache Spark

Hadoop MapReduce

MapReduce is a programming model for processing large datasets in parallel.

It has two main steps:

  • Map: divide data into smaller parts
  • Reduce: combine results into final output

It is suitable for large-scale batch processing.

YARN

YARN stands for Yet Another Resource Negotiator.

It manages computing resources in a Hadoop cluster.

Its main role is to:

  • Allocate resources
  • Manage applications
  • Improve cluster efficiency

https://yarnpkg.com

https://yarnpkg.com

Apache Spark

Apache Spark is a fast data processing engine for Big Data.

It supports:

https://spark.apache.org/

https://spark.apache.org/
  • Batch processing
  • Real-time streaming
  • Machine learning
  • Graph processing
  • SQL analytics

Spark is often faster than traditional MapReduce.

6. Data Analytics and Data Mining Tools

Data analytics and data mining tools help users discover patterns, relationships, and insights from data.

Examples:

  • Excel
  • Python
  • R
  • Jamovi
  • Orange
  • RapidMiner
  • SPSS Modeler

Excel is useful for basic data analysis and reporting.


It is suitable for:

  • Small to medium datasets
  • Pivot tables
  • Basic charts
  • Simple statistical analysis
  • Business reports

https://excel.cloud.microsoft/th-th/

https://excel.cloud.microsoft/th-th/

Python is widely used for data analytics, machine learning, and automation.

Common libraries:

  • pandas
  • NumPy
  • matplotlib
  • scikit-learn
  • TensorFlow

https://www.python.org/

https://www.python.org/

Python is suitable for advanced analytics and AI applications.

R is a programming language designed for statistics and data analysis.

It is suitable for:

  • Statistical modelling
  • Data visualisation
  • Data mining
  • Research analysis
  • Reproducible reports

https://www.r-project.org/

https://www.r-project.org/

Orange is a visual data mining tool. It is suitable for beginners because users can build workflows using drag-and-drop components.

Common uses:

  • Classification
  • Clustering
  • Data visualisation
  • Educational data mining activities

RapidMiner is a data science platform for data preparation, machine learning, and predictive analytics.

It is useful for:

  • Business analytics
  • Customer segmentation
  • Prediction models
  • Process automation

SPSS Modeler is a data mining and predictive analytics tool.

It is suitable for:

  • Business users
  • Market analysis
  • Customer behaviour analysis
  • Predictive modelling

jamovi is a free and open-source statistical analysis software built on R.

It is suitable for:

  • Statistical analysis
  • Data visualisation
  • Academic research
  • Teaching and learning

https://www.jamovi.org

https://www.jamovi.org

7. Data Visualisation Tools

Data visualisation tools help communicate data insights clearly. Common tools include:

  • Power BI
  • Tableau
  • Looker Studio
  • Plotly

Power BI is a business intelligence tool from Microsoft.

It is useful for:

  • Interactive dashboards
  • Business reports
  • Data modelling
  • Connecting with Excel and databases

Tableau is a popular data visualisation platform.

It is strong in:

  • Interactive charts
  • Dashboard design
  • Data storytelling
  • Exploratory data analysis

Looker Studio is a free dashboard and reporting tool from Google.

It is suitable for:

  • Online dashboards
  • Google Sheets data
  • Google Analytics data
  • Marketing reports

Plotly is a visualisation library used with Python, R, and JavaScript. It is suitable for:

  • Interactive charts
  • Web-based dashboards
  • Data science projects
  • Custom visualisations

Choosing the Right Tool

Task Suitable Tools
Store structured business data RDBMS
Store large flexible data NoSQL, Cloud Storage, HDFS
Process very large datasets Hadoop, Spark
Analyse data Excel, Python, R, Orange
Build dashboards Power BI, Tableau, Looker Studio
Create interactive charts Plotly

Summary

Big Data Technology helps organisations manage and use large and complex data.

Key components include:

  • Data collection
  • Data storage
  • Data processing
  • Data analytics
  • Data visualisation
  • Decision-making

The right technology depends on the business problem, data size, data type, and user skill level.