Data Integration with Cloud Data Fusion
Description
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 transformationsUse 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
Topics
Introduction
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
Building pipelines
Cloud Data Fusion architecture
Core concepts
Data pipelines and directed acyclic graphs (DAG)
Pipeline Lifecycle
Designing pipelines in Pipeline Studio
Designing complex pipelines
Branching, Merging and Joining
Actions and Notifications
Error handling and Macros
Pipeline Configurations, Scheduling, Import and Export
Pipeline execution environment
Schedules and triggers
Execution environment: Compute profile and provisioners
Monitoring pipelines
Building Transformations and Preparing Data with Wrangler
Wrangler
Directives
User-defined directives
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
Metadata and data lineage
Metadata
Data lineage
Summary
האם אתה בטוח שאתה רוצה לסגור את הטופס ולאבד את כל השינויים?
