Data Integration with Cloud Data Fusion

Module 00 - Introduction

(in English)

Module 01 - Introduction to Data Integration and Cloud Data Fusion

• Data integration: what, why, challenges
• Data integration tools used in the industry
• User personas
• Introduction to cloud-based data fusion
• Critical Data Integration Capabilities
• Cloud Data Fusion user interface components

Module 02 - Building Pipelines

• Cloud Data Fusion architecture
• Basic concepts
• Data pipelines and directed acyclic graphs (DAG)
• Pipeline Life Cycle
• Designing pipelines in Pipeline Studio

Module 03 - Designing Complex Pipelines

• Branches, merges and joins
• Actions and Notifications
• Error handling and macros Pipeline configurations, scheduling, import and export

Module 04 - Pipeline Execution Environment

• Scheduling and triggers
• Runtime environment: Compute profile and provisioners
• Pipeline Monitoring

Module 05 - Building transformations and preparing data with Wrangler

• Wrangler
• Guidelines
• User-defined directives

Module 06 - Stream Connectors and Pipelines

• Understand the data integration architecture.
• List the different connectors.
• Use the Cloud Data Loss Prevention (DLP) API.
• Understand the streaming pipeline reference architecture.
• Build and run a streaming pipeline

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Module 07 - Metadata and Data Lineage

• Metadata
• Data lineage

Module 08 - Summary

• Course summary

Complete "Fundamentals of Big Data and Machine Learning."

• Identify the need for data integration,
• Understand the capabilities of Cloud Data Fusion as a data integration platform,
• Identify use cases for possible implementation with Cloud Data Fusion,
• List the major components of Cloud Data Fusion,
• [Design and execute batch and real-time data processing pipelines,
• Work with Wrangler to build data transformations.
• Use connectors to integrate data from different sources and formats,
• Configure the runtime environment; monitor and troubleshoot pipeline execution,
• Understand the relationship between metadata and data lineage

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Complete "Fundamentals of Big Data and Machine Learning."