Mastering Data Integration: Design, Development and Best Practices with Modern Trends

Data Integration Deployment and Management, equips you with the knowledge and skills to seamlessly integrate data from various sources and unlock its full potential. Whether you're a data analyst, developer, or IT professional.

Course Overview
Course overview: Mastering Data Integration: Design, Development, and Best Practices with Modern Trends

Slide Mastering Data Integration: Design, Development, and Best Practices with Modern Trends

Module 1: Introduction to Data Integration
1.0 Introduction to Data Integration

1.1 What is data integration

1.2 Challenges, benefits and Business use cases for data integration

1.3 Introduction to data integration Architectures and Business Intelligence (BI)

Module 2: Data Integration Analysis

2.1 Defining data integration requirements

2.2 The Source system analysis and profiling

2.3 Data quality assessment and cleansing techniques

2.4 Data volume analysis and target system mapping

2.5 Introduction to data integration modeling concepts

Module 3: Data Integration Design

3.0 Overview about Data Integration Design

3.1 Macro design best practices: Source system selection and prioritization

3.2 Macro design best practices: Data transformation strategies

3.3 Macro design best practices: Target system design considerations

3.4 Micro design best practices: Component-based design principles

3.5 Micro design best practices: Physical data integration modeling techniques

3.6 Micro design best practices: Coding standards and documentation practices

3.7 Micro design best practices: Data security and access control considerations

Module 4: Data Integration Development

4.0 Overview about Data Integration Development

4.1 Data extraction techniques (full vs. incremental loads)

4.2 Change data capture (CDC) methods

4.3 Error handling and data integrity checks

4.4 Data transformation and cleansing in development environments

4.5 Unit testing and integration testing strategies for data integration processes

Module 5: Data Integration Deployment and Management

5.0 Overview about Data Integration Deployment and Management

5.1 Building and deploying data integration pipelines

5.2 Continuous integration and continuous delivery (CI/CD) for data integration

5.3 Data integration monitoring and performance optimization techniques

5.4 Production support considerations and troubleshooting procedures

Module 6: Advanced Data Integration Topics with Modern Trends

6.0 Overview about Advanced Data Integration Topics with Modern Trends

6.1 Real-time data integration best practices (including Apache Kafka, Apache Flink, and real-time streaming technologies)

6.2 Big data integration challenges and solutions (focusing on distributed processing frameworks like Hadoop and Spark)

6.3 Cloud-based data integration platforms (exploring tools like AWS Glue, Azure Data Factory, and Google Cloud Dataflow)

6.4 Data integration governance and metadata management (including tools for data lineage tracking and cataloging)

6.5 Emerging trends in data integration: Integration with Artificial Intelligence (AI) and Machine Learning (ML) for data quality improvement and automated data transformations

6.6 Emerging trends in data integration: Serverless data integration solutions

6.7 Emerging trends in data integration: The rise of low-code/no-code data integration tools