GCP200CDF
Data Integration with Cloud Data Fusion
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.
What you will learn
- 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
- Completed "Introduction to Data Engineering"
Target audience
- Data Engineer, Data Analysts
Training Program
9 modules to master the fundamentals
Objectives
- Introduce the course objectives
Topics covered
- →Course Introduction
Objectives
- 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
Topics covered
- →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
Activities
Graded lab
quiz
discussion activity
Objectives
- 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
Topics covered
- →Cloud Data Fusion architecture
- →Core concepts
- →Data pipelines and directed acyclic graphs (DAG)
- →Pipeline Lifecycle
- →Designing pipelines in Pipeline Studio
Activities
Graded lab and quiz
Objectives
- 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
Topics covered
- →Branching, Merging and Joining
- →Actions and Notifications
- →Error handling and Macros
- →Pipeline Configurations, Scheduling, Import and Export
Activities
Graded labs and quiz
Objectives
- 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
Topics covered
- →Schedules and triggers
- →Execution environment: Compute profile and provisioners
- →Monitoring pipelines
Activities
Quiz
Objectives
- 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
Topics covered
- →Wrangler
- →Directives
- →User-defined directives
Activities
Graded lab and quiz
Objectives
- Understand the data integration architecture
- List various connectors
- Use the Cloud Data Loss Prevention (DLP) API
- Understand the reference architecture of streaming pipelines
- Build and execute a streaming pipeline
Topics covered
- →Connectors
- →DLP
- →Reference architecture for streaming applications
- →Building streaming pipelines
Activities
Graded lab
quiz
discussion activity
Objectives
- 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
Topics covered
- →Metadata
- →Data lineage
Activities
Graded lab and quiz
Objectives
- Review the course objectives & concepts
Topics covered
- →Course Summary
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
Teaching Methods Used
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Guided Labs — Guided practical exercises on software, hardware, or technical environments.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Train multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote