Data Integration and API Development for AI Applications
1h 33mIntermediate2025-05-28
Authors
Janani Ravi
Certified Google Cloud Architect and Data Engineer
Course details
This course outlines the skills you need to successfully connect diverse data sources and build APIs that enable seamless interaction between AI models and applications. Join instructor Janani Ravi as she covers essential techniques for efficient data integration and API design, and shows you how to ensure robust communication in AI-driven systems. This course is an ideal fit for anyone working with AI, data infrastructure, and machine learning operations (MLOps), including data engineers, AI and ML engineers, and software developers.
Skills covered
Data GovernanceAPIsData EngineeringArtificial Intelligence FoundationsArtificial Intelligence (AI)Data ScienceSoftware DevelopmentOne-Off
Concepts
0. Introduction
- 01 - The need for data integration and APIs
- 02 - Prerequisites
- 03 - The need for data integration in AI
- 04 - Data silos
1. Approaches to Data Integration
- 05 - Phases of data integration - Data sources
- 06 - Phases of data integration - Data ingestion
- 07 - Phases of data integration - Data mapping, transformation, and loading
- 08 - Methods of data integration
- 09 - ETL and ELT
- 10 - Streaming integration and change data capture
- 11 - Best practices and challenges in data integration
2. Integrate Data into a Unified Analytics Platform
- 12 - Uploading data to Azure Blob storage
- 13 - Ingesting data into Microsoft Fabric using shortcuts
- 14 - ETL with Fabric dataflows
3. API Development for AI Applications
- 15 - APIs to work with data and AI
- 16 - API types, benefits, and best practices
- 17 - Designing APIs
- 18 - Steps in API design
- 19 - API gateways
- 20 - Capabilities and benefits of API gateways
Conclusion
- 21 - Summary and further study