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.
Who should attend
- Data Engineer
- Data Analysts
Complete "Big Data and Machine Learning Fundamentals"
- Identify the need of 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