This 2-day course introduces learners to Google Cloud’s data integration capability using Cloud Data Fusion. 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.
Objetivos
Este curso enseña a los participantes las siguientes habilidades:
- Identify the need of data integration.
- Understand the capabilities Cloud Data Fusion provides as a dat 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.
Público
Esta clase está dirigida a la siguiente audiencia: Data Engineer Data Analysts
Prerrequisitos
Para aprovechar al máximo este curso, los participantes deben cumplir con los siguientes criterios: Completed Big Data and Machine Learning Fundamentals
Duración
Duración
2 dias
Inversión
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Resumen del curso
- Course Introduction
- 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
- Cloud Data Fusion architecture
- Core concepts
- Data pipelines and directed acyclic graphs (DAG)
- Pipeline Lifecycle
- Designing pipelines in Pipeline Studio
- Branching, Merging and Joining
- Actions and Notifications
- Error handling and Macros
- Pipeline Configurations, Scheduling, Import and Export
- Schedules and triggers
- Execution environment: Compute profile and provisioners
- Monitoring pipelines
- Wrangler
- Directives
- User-defined directives
- 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
- Data lineage
- Course Summary