Mastering Data Integration Patterns

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Dien Pham

35:51

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1 - Introduction

1 -Introduction.mp4

03:21

2 - Module 1 Introduction to Data Integration

1 -Introduction to Data Integration.mp4

01:56

2 -What is data integration.mp4

02:22

3 -Challenges and benefits of data integration.mp4

02:04

4 -Business use cases for data integration.mp4

02:36

5 -Introduction to data integration architectures (ETL, ELT, Data Vault).mp4

02:37

6 -Introduction to Business Intelligence (BI) and its relationship to data integrat.mp4

00:47

3 - Module 2 Data Integration Analysis

1 -Data Integration Analysis - Understand Business Needs & Key Questions.mp4

03:26

2 -Benefits of Clearly Defined Requirements.mp4

01:08

3 -Tools and Techniques for Defining Requirements.mp4

01:30

4 -Source system analysis and profilin.mp4

00:31

5 -Why Analyze and Profile Source Systems.mp4

01:11

6 -The Source System Analysis and Profiling Process.mp4

01:34

7 -Data Profiling Tools and Techniques.mp4

00:58

8 -Documenting Your Findings.mp4

00:49

9 -Benefits of Source System Analysis and Profiling.mp4

02:32

10 -Why Focus on Data Quality.mp4

01:24

11 -Techniques for Data Quality Assessment.mp4

01:13

12 -Common Data Cleansing Technique.mp4

01:24

13 -Data Quality Tools and Technologies.mp4

02:28

Description

Mastering Data Integration: Design, Development and Management

What You'll Learn?

Understand the fundamentals of data integration and its applications in business intelligence.
Analyze and design data integration architectures and models.
Develop robust data integration pipelines using best practices for data extraction, transformation, and loading.
Deploy and manage data integration solutions with modern deployment practices (CI/CD).
Tackle advanced integration challenges such as real-time and big data integration.
Keep up with emerging trends in cloud-based platforms and governance techniques.

Who is this for?

Data professionals (data analysts, data engineers) seeking to expand their knowledge of integration processes.

IT professionals involved in managing or implementing data systems.

Developers and engineers interested in learning more about ETL processes and data management.

Business intelligence professionals wanting to deepen their understanding of how integrated data supports BI systems.

Anyone looking to build or improve data integration solutions within their organization.

What You Need to Know?

Basic Understanding of Databases and SQL

Familiarity with Data Management Concepts (Preferred)

Knowledge of Business Intelligence (BI) Concepts (Preferred)

Exposure to ETL Tools and Technologies (Optional)

Basic Understanding of Cloud Computing (Optional)

More details

Description
This six-module course takes you through every stage of data integration, from fundamental concepts to advanced techniques and modern trends. You will learn to analyze, design, develop, deploy, and manage data integration solutions that enhance business intelligence and unlock the power of your data assets.
Course Modules:
Module 1: Introduction to Data Integration
This introductory module lays the foundation for understanding data integration.
Key Topics:
What is data integration?
Challenges and benefits of data integration.
Business use cases for data integration.
Introduction to data integration architectures: ETL, ELT, Data Vault.
Introduction to Business Intelligence (BI) and its relationship to data integration.
Module 2: Data Integration Analysis
This module focuses on analyzing your data environment and defining integration needs.
Key Topics:
Defining data integration requirements.
Source system analysis and profiling.
Data quality assessment and cleansing techniques.
Data volume analysis and target system mapping.
Introduction to data integration modeling concepts.
Module 3: Data Integration Design
Learn how to design an effective data integration solution in this module.
Macro Design Best Practices:
Source system selection and prioritization.
Data transformation strategies.
Target system design considerations.
Micro Design Best Practices:
Component-based design principles.
Physical data integration modeling techniques.
Coding standards and documentation practices.
Data security and access control considerations.
Module 4: Data Integration Development
This module covers the development phase, transforming design into action.
Key Topics:
Data extraction techniques: full vs. incremental loads.
Change data capture (CDC) methods.
Error handling and data integrity checks.
Data transformation and cleansing in development environments.
Unit testing and integration testing strategies for data integration processes.
Module 5: Data Integration Deployment and Management
Effective deployment and management are crucial for sustainable data integration.
Key Topics:
Building and deploying data integration pipelines.
Continuous integration and continuous delivery (CI/CD) for data integration.
Data integration monitoring and performance optimization techniques.
Production support considerations and troubleshooting procedures.
Module 6: Advanced Data Integration Topics with Modern Trends
The final module explores advanced data integration concepts and emerging trends.
Key Topics:
Real-time data integration best practices.
Big data integration challenges and solutions.
Cloud-based data integration platforms.
Data integration governance and metadata management.
Emerging trends in data integration.
Who this course is for:
Data professionals (data analysts, data engineers) seeking to expand their knowledge of integration processes.
IT professionals involved in managing or implementing data systems.
Developers and engineers interested in learning more about ETL processes and data management.
Business intelligence professionals wanting to deepen their understanding of how integrated data supports BI systems.
Anyone looking to build or improve data integration solutions within their organization.

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Data Science

Dien Pham

Instructor's Courses

I am an experienced and accomplished author with a deep passion for data strategy and management. With over 15 years of hands-on experience in the IT industry, I have gained extensive knowledge and expertise in various domains, including banking, insurance, retail, and more.Throughout my career, I have held several senior positions such as:- Data Division Director of an insurance company- Deputy Data Governance & Analytics of the Group (Real Estate, Retail, Hospitality, Commerce, ...)- Deputy Chief Data Officer cum Senior IT Strategy Expert- Head of Data & Analytics Service- Project Director/ Project Manager- Enterprise & Solution Architect of bankThat have allowed me to make significant contributions to the field. And right now, as a director of Data Division, I played a pivotal role in shaping data initiatives and driving the effective use of data within organizations. Additionally, as a Data Governance & Analytics expert, I focused on establishing robust data governance frameworks and harnessing the power of analytics to drive actionable insights.With my diverse background and extensive industry experience, I have developed a strong understanding of the challenges and opportunities in the data management landscape. Through my work as an author, I am dedicated to sharing my knowledge, insights, and practical strategies with students like you.

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