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
• Cloud 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