This two-day course introduces learners to Google Cloud’s data integration capability using Cloud Data Fusion.
2 day course
Supporting material
Google Cloud Partner of the Year
Private
Private
A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.
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.
Jellyfish has recently been named a Google Cloud Specialisation Partner of the Year. This title recognises our commitment to provide world-leading Cloud-based Training solutions that help our clients succeed.
Course overview
Who should attend:
This course is primarily intended for Data Engineers and Data Analysts who use the Google Cloud Platform.
Walk away with the ability to:
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 the Google Cloud Fundamentals: Big Data and Machine Learning course or have the equivalent knowledge and experience.
Course Agenda
Module 1: Introduction
Introducing the course objectives
Module 2: 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 3: Building Pipelines
Cloud Data Fusion architecture
Core concepts
Data pipelines and directed acyclic graphs (DAG)
Pipeline Lifecycle
Designing pipelines in Pipeline Studio
Module 4: Designing Complex Pipelines
Branching, merging and joining
Actions and notifications
Error handling and macros
Pipeline configurations, Scheduling, Import and Export
Module 5: Pipeline Execution Environment
Schedules and triggers
Execution environment: Compute profile and provisioners
Monitoring pipelines
Module 6: Building Transformations and Preparing Data with Wrangler
Wrangler
Directives
User-defined directives
Module 7: Connectors and Streaming Pipelines
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
Module 8: Metadata and data lineage
Metadata
Data lineage
Module 8: Summary
A summary of what was learned and covered in the course
2 day course
Supporting material
Google Cloud Partner of the Year
Private