SQL for Data Integration
( 30 Modules )

Module #1
Introduction to SQL and Data Integration
Overview of SQL, data integration, and the importance of integrating data from multiple sources

Module #2
SQL Fundamentals
Basic syntax and data types in SQL, including SELECT, FROM, WHERE, GROUP BY, and HAVING

Module #3
Data Modeling for Data Integration
Concepts of data modeling, entity-relationship diagrams, and data warehousing

Module #4
SQL Joins and Subqueries
Types of joins (inner, outer, left, right), subqueries, and common table expressions (CTEs)

Module #5
Data Normalization and Denormalization
First normal form (1NF), second normal form (2NF), third normal form (3NF), and denormalization techniques

Module #6
Data Profiling and Quality
Understanding data quality issues, data profiling techniques, and data quality metrics

Module #7
ETL (Extract, Transform, Load) Basics
Overview of ETL, data extraction, data transformation, and data loading

Module #8
SQL for Data Extraction
Using SQL for data extraction, including querying, filtering, and aggregating data

Module #9
Data Transformation Techniques
Data transformation methods, including data aggregation, data merging, and data sorting

Module #10
Data Loading and Target Systems
Data loading techniques, target system design, and data warehousing architectures

Module #11
Advanced SQL Topics
Advanced SQL concepts, including window functions, Common Table Expressions (CTEs), and JSON/XML support

Module #12
Data Integration Patterns
Common data integration patterns, including data consolidation, data federation, and data propagation

Module #13
Data Governance and Security
Data governance principles, data security measures, and access control mechanisms

Module #14
SQL for Data Quality and Cleansing
Using SQL for data quality and data cleansing, including data profiling and data validation

Module #15
Data Integration Tools and Technologies
Overview of popular data integration tools and technologies, including Informatica, Talend, and Microsoft SSIS

Module #16
Real-World Data Integration Scenarios
Case studies and real-world examples of data integration, including customer data integration and supply chain integration

Module #17
Optimizing SQL for Performance
Best practices for optimizing SQL queries for performance, including indexing, caching, and query optimization

Module #18
Data Integration Best Practices
Best practices for data integration, including data governance, data quality, and data security

Module #19
SQL for Big Data and NoSQL
Using SQL for big data and NoSQL databases, including Hive, Spark SQL, and MongoDB

Module #20
Cloud-Based Data Integration
Cloud-based data integration, including cloud-based ETL, cloud data warehousing, and cloud-native data integration

Module #21
Real-Time Data Integration
Real-time data integration, including streaming data integration, event-driven architecture, and change data capture

Module #22
Data Integration for Machine Learning
Data integration for machine learning, including data preparation, data augmentation, and data labeling

Module #23
SQL for Data Visualization
Using SQL for data visualization, including data preparation, data aggregation, and data visualization tools

Module #24
Data Integration for IoT and Edge Computing
Data integration for IoT and edge computing, including device data integration, edge analytics, and real-time data processing

Module #25
SQL for Graph Databases
Using SQL for graph databases, including graph data models, graph queries, and graph data integration

Module #26
Data Integration for Customer 360
Data integration for customer 360, including customer data integration, customer profiling, and customer analytics

Module #27
SQL for Supply Chain Integration
Using SQL for supply chain integration, including data integration for supply chain management, logistics, and manufacturing

Module #28
Data Integration for Healthcare
Data integration for healthcare, including electronic health records (EHRs), healthcare data analytics, and medical research

Module #29
SQL for Financial Data Integration
Using SQL for financial data integration, including financial data reporting, financial analytics, and risk management

Module #30
Course Wrap-Up & Conclusion
Planning next steps in SQL for Data Integration career

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SQL for Data Integration ( 30 Modules )

SQL for Data Integration
( 30 Modules )