Big Data Modeling and Management Systems

By Alexander Afriyie

Introduction

Welcome to the training course on Big Data Modelling and Management Systems. The purpose of this course is to provide an understanding of the principles of big data modelling, management systems, and their applications in solving real-world problems. Through a combination of lecture, discussion, and practical exercises, you will gain a comprehensive overview of big data manipulation techniques and tools.

You will learn how to implement effective solutions to build sustainable models for your organization's data. Furthermore, you will be exposed to the best practices and industry standards related to data governance, quality assurance processes, as well as advanced analytics.

By the end of this course, you should be able to use big data modelling approaches confidently in order to improve organizational performance.

Objectives

1. To provide participants with an understanding of the fundamentals of Big Data Modelling and Management Systems.

2. To develop participant's skills in using data management tools to analyse, organize, and store data for maximum efficiency.

3. To learn about best practices when designing and implementing Big Data models for better performance and scalability.

4. To equip participants with the knowledge of how to integrate different applications into a cohesive system that leverages organizational data resources efficiently.

5. To be able to measure, monitor, and track performance metrics from a variety of sources as part of managing structural complexity associated with Big Data analytics projects.

Course Outline

Module 1: Overview of Big Data and Modelling

• Understanding of the fundamentals of Big Data Modelling and Management Systems.

• Introduction to Big Data & its sources

• Understanding the key features of Big Data & why it is important

• Overview of different modelling techniques used in Big Data

• Overview of big data tools

• Understanding the importance of data pre-processing 

•Skills in using data management tools to analyse, organize, and store data for maximum efficiency.

Module 2: Working with Different Types of Database Structures

• Database fundamentals & basic SQL commands

• Understanding NoSQL databases and usage Scenarios 

• Working with Apache Hadoop Distributed File System (HDFS) 

 Module 3: Granularizing, Normalizing, and Summarizing Big Data

• Techniques for granularizing large datasets for analysis 

• Extracting information from unstructured data sources  

• Working with normalization methods such as z-score standardization & min-max scaling     

• Applying feature extraction techniques such as principal component analysis (PCA)  

 Module 4: Building a Big Data Modelling System

• Implementing predictive analytics using machine learning algorithms   

• Utilizing various big data analytics tools e.g., Apache Spark, Google Cloud Platform (GCP), etc.    

• Creating a data pipeline architecture for model building     

• Exploring best practices associated with model management systems

• Best practices when designing and implementing Big Data models for better performance and scalability.

 •  Exposed to the best practices and industry standards related to data governance, quality assurance processes, as well as advanced analytics.

• Using supervised & unsupervised learning techniques to evaluate models     

 Module 5: Assessing and Deploying a Model Management System

 

• Language: English
• Publisher: Alexander Afriyie
• Release date: Aug 12, 2023
• ISBN: 9798223343103

Book preview

Big Data Modeling and Management Systems

Introduction

Welcome to the training course on Big Data Modelling and Management Systems. The purpose of this course is to provide an understanding of the principles of big data modelling, management systems, and their applications in solving real-world problems. Through a combination of lecture, discussion, and practical exercises, you will gain a comprehensive overview of big data manipulation techniques and tools.

You will learn how to implement effective solutions to build sustainable models for your organization's data. Furthermore, you will be exposed to the best practices and industry standards related to data governance, quality assurance processes, as well as advanced analytics.

By the end of this course, you should be able to use big data modelling approaches confidently in order to improve organizational performance.

Objectives

1. To provide participants with an understanding of the fundamentals of Big Data Modelling and Management Systems.

2. To develop participant’s skills in using data management tools to analyse, organize, and store data for maximum efficiency.

3. To learn about best practices when designing and implementing Big Data models for better performance and scalability.

4. To equip participants with the knowledge of how to integrate different applications into a cohesive system that leverages organizational data resources efficiently.

5. To be able to measure, monitor, and track performance metrics from a variety of sources as part of managing structural complexity associated with Big Data analytics projects.

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Course Outline

Module 1: Overview of Big Data and Modelling

• Understanding of the fundamentals of Big Data Modelling and Management Systems.

• Introduction to Big Data & its sources

• Understanding the key features of Big Data & why it is important

• Overview of different modelling techniques used in Big Data

• Overview of big data tools

• Understanding the importance of data pre-processing 

•Skills in using data management tools to analyse, organize, and store data for maximum efficiency.

Module 2: Working with Different Types of Database Structures

• Database fundamentals & basic SQL commands

• Understanding NoSQL databases and usage Scenarios 

• Working with Apache Hadoop Distributed File System (HDFS) 

Module 3: Granularizing, Normalizing, and Summarizing Big Data

• Techniques for granularizing large datasets for analysis 

• Extracting information from unstructured data sources 

• Working with normalization methods such as z-score standardization & min-max scaling 

• Applying feature extraction techniques such as principal component analysis (PCA) 

Module 4: Building a Big Data Modelling System

• Implementing predictive analytics using machine learning algorithms 

• Utilizing various big data analytics tools e.g., Apache Spark, Google Cloud Platform (GCP), etc. 

• Creating a data pipeline architecture for model building 

• Exploring best practices associated with model management systems

• Best practices when designing and implementing Big Data models for better performance and scalability.

•  Exposed to the best practices and industry standards related to data governance, quality assurance processes, as well as advanced analytics.

• Using supervised & unsupervised learning techniques to evaluate models 

Module 5: Assessing and Deploying a Model Management System

•  How to implement effective solutions to build sustainable models for your organization's data 

• Deployment options & considerations when deploying the system 

• Managing input data to ensure