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. Data Integration with Cloud Data Fusion Benefits Important Course Information: 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. Prerequisites: To get the most out of this course, participants are encouraged to have completed “Introduction to Data Engineering” Data Integration with Google Cloud Data Fusion Training Outline Learning Objectives Module 1) Introduction to data integration and Cloud Data Fusion Understand the need for data integration, List the situations/cases where data integration can help businesses, List the available data integration platforms and tools, Identify the challenges with data integration Understand the use of Cloud Data Fusion as a data integration platform Create a Cloud Data Fusion instance, Familiarize with core framework and major components in Cloud Data Fusion Module 2) Building Pipelines Understand Cloud Data Fusion architecture Define what a data pipeline is Understand the DAG representation of a data pipeline, Learn to use Pipeline Studio and its components Design a simple pipeline using Pipeline Studio, Deploy and execute a pipeline Module 3) Designing complex pipelines Perform branching, merging, and join operations. Execute pipeline with runtime arguments using macros. Work with error handlers. Execute pre- and post-pipeline executions with help of actions and notifications. Schedule pipelines for execution. Import and export existing pipelines. Module 4) Pipeline execution environment Understand the composition of an execution environment. Configure your pipeline’s execution environment, logging, and metrics. Understand concepts like compute profile and provisioner. Create a compute profile. Create pipeline alerts. Monitor the pipeline under execution. Module 5) Building Transformations and Preparing Data with Wrangler Understand the use of Wrangler and its main components. Transform data using Wrangler UI. Transform data using directives/CLI methods. Create and use user-defined directives. Module 6) Connectors and streaming pipelines Connectors DLP Reference architecture for streaming applications Building streaming pipelines Module 7) Metadata and data lineage List types of metadata. Differentiate between business, technical, and operational metadata. Understand what data lineage is. Understand the importance of maintaining data lineage. Differentiate between metadata and data lineage
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