Mastering Data Integration Patterns

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.

Requirements

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)

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 IntegrationThis 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 AnalysisThis 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 DesignLearn 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 DevelopmentThis 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 ManagementEffective 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 TrendsThe 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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Module 1: Introduction to Data Integration

Lecture 2 Introduction to Data Integration

Lecture 3 What is data integration?

Lecture 4 Challenges and benefits of data integration

Lecture 5 Business use cases for data integration

Lecture 6 Introduction to data integration architectures (ETL, ELT, Data Vault)

Lecture 7 Introduction to Business Intelligence (BI) and its relationship to data integrat

Section 3: Module 2: Data Integration Analysis

Lecture 8 Data Integration Analysis - Understand Business Needs & Key Questions

Lecture 9 Benefits of Clearly Defined Requirements

Lecture 10 Tools and Techniques for Defining Requirements

Lecture 11 Source system analysis and profilin

Lecture 12 Why Analyze and Profile Source Systems

Lecture 13 The Source System Analysis and Profiling Process

Lecture 14 Data Profiling Tools and Techniques

Lecture 15 Documenting Your Findings

Lecture 16 Benefits of Source System Analysis and Profiling

Lecture 17 Why Focus on Data Quality?

Lecture 18 Techniques for Data Quality Assessment

Lecture 19 Common Data Cleansing Technique

Lecture 20 Data Quality Tools and Technologies

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.