This 2-day course introduces learners to Google Cloud’s data integration capability
using Cloud Data Fusion. In this course, we discuss challenges with data integration
and the need for a data integration platform (middleware). We then discuss how
Cloud Data Fusion can help to effectively integrate data from a variety of sources
and formats and generate insights. We take a look at Cloud Data Fusion’s main
components and how they work, how to process batch data and real time streaming
data with visual pipeline design, rich tracking of metadata and data lineage, and how
to deploy data pipelines on various execution engines.

• Identify the need for data integration
• Understand the capabilities Cloud Data Fusion provides as a data integration platform
• Identify use cases for possible implementation with Cloud Data Fusion
• List the core 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 various sources and formats
• Configure execution environment; Monitor and Troubleshoot pipeline execution
• Understand the relationship between metadata and data lineage

Audience

• Data Engineers
• Data Analysts

Prerequisites

To get the most out of this course, participants are encouraged to have:

• Completed “Big Data and Machine Learning Fundamentals

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Module 1: Introduction to data integration and Cloud Data Fusion

• Data integration: what, why, challenges
• Data integration tools used in industry
• User personas
• Introduction to Cloud Data Fusion
• Data integration critical capabilities
• Cloud Data Fusion UI components

Module​ ​​2:​​ Building pipelines

• Cloud Data Fusion architecture
• Core concepts
• Data pipelines and directed acyclic graphs (DAG)
• Pipeline Lifecycle
• Designing pipelines in Pipeline Studio