Data Integration with Cloud Data Fusion (DICDF)

 

Course Overview

This 2-day course introduces learners to the data integration capability of Google Cloud using Cloud Data Fusion. In this course, we discuss the challenges of data integration and the need for a data integration platform (middleware). We then examine how Cloud Data Fusion can help effectively integrate data from a variety of sources and formats and generate insights. We look at the main components of Cloud Data Fusion and how they work, how to process batch and streaming data in real time with visual pipeline design, rich metadata and data lineage tracking, and how to deploy data pipelines on various runtime engines.

Who should attend

  • Data Engineer
  • Data Analysts

Prerequisites

Complete "Fundamentals of Big Data and Machine Learning."

Course Objectives

  • Identify the need for data integration,
  • Understand the capabilities of Cloud Data Fusion as a data integration platform,
  • Identify use cases for possible implementation with Cloud Data Fusion,
  • List the major 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 different sources and formats,
  • Configure the runtime environment; monitor and troubleshoot pipeline execution,
  • Understand the relationship between metadata and data lineage

.

Course Content

Module 00 - Introduction

(in English)

Module 01 - Introduction to Data Integration and Cloud Data Fusion
  • Data integration: what, why, challenges
  • Data integration tools used in the industry
  • User personas
  • Introduction to cloud-based data fusion
  • Critical Data Integration Capabilities
  • Cloud Data Fusion user interface components
Module 02 - Building Pipelines
  • Cloud Data Fusion architecture
  • Basic concepts
  • Data pipelines and directed acyclic graphs (DAG)
  • Pipeline Life Cycle
  • Designing pipelines in Pipeline Studio
Module 03 - Designing Complex Pipelines
  • Branches, merges and joins
  • Actions and Notifications
  • Error handling and macros Pipeline configurations, scheduling, import and export
Module 04 - Pipeline Execution Environment
  • Scheduling and triggers
  • Runtime environment: Compute profile and provisioners
  • Pipeline Monitoring
Module 05 - Building transformations and preparing data with Wrangler
  • Wrangler
  • Guidelines
  • User-defined directives
Module 06 - Stream Connectors and Pipelines
  • Understand the data integration architecture.
  • List the different connectors.
  • Use the Cloud Data Loss Prevention (DLP) API.
  • Understand the streaming pipeline reference architecture.
  • Build and run a streaming pipeline

.

Module 07 - Metadata and Data Lineage
  • Metadata
  • Data lineage
Module 08 - Summary
  • Course summary
Online training
Modality: L

Süre 2 Gün

Classroom training
Modality: C

Süre 2 Gün

Kurs tarihleri

Ingilizce
2 hours difference
Online training This is a FLEX course.
saat dilimi: British Summer Time (BST)
3 hours difference
Online training This is a FLEX course.
saat dilimi: Greenwich Mean Time (GMT)
This is a FLEX course, which is delivered both virtually and in the classroom.
Büyük Britanya
Online training This is an Ingilizce language FLEX course.   saat dilimi: Europe/London Kurs dili: Ingilizce Enroll
Online training This is an Ingilizce language FLEX course.   saat dilimi: Europe/London Kurs dili: Ingilizce Enroll
This is a FLEX course, which is delivered both virtually and in the classroom.