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Data Integration with Cloud Data Fusion

WGAC-GGL-DICDF

Google
Open

Data Integration with Cloud Data Fusion

22 Sep 2022 - 2 days

English

GMT UTC+00:00

£1,150

Open

Data Integration with Cloud Data Fusion

15 Dec 2022 - 2 days

English

GMT UTC+00:00

£1,150

Description

Show Tabs
Introduction
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
Prerequisites & Audience

Complete "Fundamentals of Big Data and Machine Learning."

Course Benefits
  • 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 Topics
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

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