Data Integration with Cloud Data Fusion

Name: Data Integration with Cloud Data Fusion
Price: 1000.00 USD

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

A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

Jellyfish is committed to providing world-leading Cloud-based Training solutions that help clients succeed. In this course, we discuss challenges with data integration and the need for a data integration platform.

You’ll learn how Cloud Data Fusion can help to effectively integrate data from a variety of sources and formats, as well as generate insights. We’ll 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.

Our Data Integration with Cloud Data Fusion course is available as a private training session that can be delivered via Virtual Classroom or at a location of your choice in the US.

Course overview

Who should attend:

This course is primarily intended for data engineers and data analysts who use the Google Cloud platform.

What you'll learn:

By the end of this course, you will be able 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, you should 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 & 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 & Preparing Data with Wrangler

Wrangler
Directives
User-defined directives

Module 7: Connectors & 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 & Data Lineage

Metadata
Data lineage

Module 9: Summary

A summary of what was learned and covered in the course

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