Course Overview
This 2-day course introduces learners to the data integration capability of Google Cloud using Cloud Data Fusion. In this course, we discuss the challenges of data integration and the need for a data integration platform (middleware). We then examine how Cloud Data Fusion can help effectively integrate data from a variety of sources and formats and generate insights. We look at the main components of Cloud Data Fusion and how they work, how to process batch and streaming data in real time with visual pipeline design, rich metadata and data lineage tracking, and how to deploy data pipelines on various runtime engines.
Who should attend
- Data Engineer
- Data Analysts
Prerequisites
To get the most out of this course, participants are encouraged to have: Completed Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM)
Course Objectives
- 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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