Introduction to Data Engineering on Google Cloud (IDEG)

Outline: Introduction to Data Engineering on Google Cloud (IDEG)
Module 1 - Data Engineering Tasks and Components
Topics:
• The role of a data engineer
• Data sources versus data sinks
• Data formats
• Storage solution options on Google Cloud
• Metadata management options on Google Cloud
• Sharing datasets using Analytics Hub
Objectives:
• Explain the role of a data engineer.
• Understand the differences between a data source and a data sink.
• Explain the different types of data formats.
• Explain the storage solution options on Google Cloud.
• Learn about the metadata management options on Google Cloud.
• Understand how to share datasets with ease using Analytics Hub.
• Understand how to load data into BigQuery using the Google Cloud console or the gcloud CLI.
Activities:
• Lab: Loading Data into BigQuery
• Quiz

Module 2 - Data Replication and Migration
Topics:
• Replication and migration architecture
• The gcloud command-line tool
• Moving datasets
• Datastream
Objectives:
• Explain the baseline Google Cloud data replication and migration architecture.
• Understand the options and use cases for the gcloud command-line tool.
• Explain the functionality and use cases for Storage Transfer Service.
• Explain the functionality and use cases for Transfer Appliance.
• Understand the features and deployment of Datastream.
Activities:
• Lab: Datastream: PostgreSQL Replication to BigQuery (optional for ILT)
• Quiz

Module 3 - The Extract and Load Data Pipeline Pattern
Topics:
• Extract and load architecture
• The bq command-line tool
• BigQuery Data Transfer Service
• BigLake
Objectives:
• Explain the baseline extract and load architecture diagram.
• Understand the options of the bq command-line tool.
• Explain the functionality and use cases for BigQuery Data Transfer Service.
• Explain the functionality and use cases for BigLake as a non-extract-load pattern.
Activities:
• Lab: BigLake: Qwik Start
• Quiz

Module 4 - The Extract, Load, and Transform Data Pipeline Pattern
Topics:
• Extract, load, and transform (ELT) architecture
• SQL scripting and scheduling with BigQuery
Dataform
Objectives:
• Explain the baseline extract, load, and transform architecture diagram.
• Understand a common ELT pipeline on Google Cloud.
• Learn about BigQuery’s SQL scripting and scheduling capabilities.
• Explain the functionality and use cases for Dataform.
Activities:
• Lab: Create and Execute a SQL Workflow in Dataform
• Quiz

Module 5 - The Extract, Transform, and Load Data Pipeline Pattern
Topics:
• Extract, transform, and load (ETL) architecture
• Google Cloud GUI tools for ETL data pipelines
• Batch data processing using Dataproc
• Streaming data processing options
• Bigtable and data pipelines
Objectives:
• Explain the baseline extract, transform, and load architecture diagram.
• Learn about the GUI tools on Google Cloud used for ETL data pipelines.
• Explain batch data processing using Dataproc.
• Learn how to use Dataproc Serverless for Spark for ETL.
• Explain streaming data processing options.
• Explain the role Bigtable plays in data pipelines.
Activities:
• Lab: Use Dataproc Serverless for Spark to Load BigQuery (optional for ILT)
• Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow
• Quiz

Module 6 - Automation Techniques
Topics:
• Automation patterns and options for pipelines
• Cloud Scheduler and Workflows
• Cloud Composer
vCloud Run Functions
• Eventarc
Objectives:
• Explain the automation patterns and options available for pipelines.
• Learn about Cloud Scheduler and Workflows.
• Learn about Cloud Composer.
• Learn about Cloud Run functions.
• Explain the functionality and automation use cases for Eventarc.
Activities:
• Lab: Use Cloud Run Functions to Load BigQuery (optional for ILT)
• Quiz