Course Outline

Introduction to Make for Data Integration

  • Overview of Make and its capabilities
  • Understanding automation and data workflows
  • Exploring use cases for data integration

Building Automated Data Pipelines

  • Designing data workflows with Make
  • Connecting databases, CRMs, and business applications
  • Configuring triggers, actions, and conditions

Real-Time Data Synchronization

  • Syncing data between multiple platforms
  • Ensuring data consistency and accuracy
  • Handling data conflicts and errors

Data Transformation and Processing

  • Applying filters, formatters, and aggregators
  • Structuring and cleaning incoming data
  • Enhancing data quality through automation

Advanced Automation Techniques

  • Using APIs and webhooks for dynamic integrations
  • Creating multi-step automation workflows
  • Implementing conditional logic in data automation

Monitoring and Optimizing Workflows

  • Tracking automation performance
  • Troubleshooting errors and debugging issues
  • Best practices for efficient data integration

Practical Implementation and Use Cases

  • Hands-on project: Creating a real-world data automation workflow
  • Case studies on successful data integration with Make
  • Discussion on scaling automation strategies

Summary and Next Steps

Requirements

  • Basic understanding of data integration concepts
  • Experience with data management or business intelligence tools
  • Familiarity with APIs and automation tools is beneficial but not required

Audience

  • Data analysts
  • Data engineers
  • IT teams
 14 Hours

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