Introduction to Data Engineering on Google Cloud

- Data engineers

- Database administrators

- System administrators

After this course participants should be able to:

• Understand the role of a data engineer.
• Identify data engineering tasks and core components used on Google Cloud.
• Understand how to create and deploy data pipelines of varying patterns on Google Cloud.
• Identify and utilize various automation techniques on Google Cloud.

Module 01: Data Engineering Tasks and Components

• 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.
• Lab: Loading Data into BigQuery
• Quiz

Module 02: Data Replication and Migration

• 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.
• Lab: Datastream: PostgreSQL Replication to BigQuery (optional for ILT)
• Quiz

Module 03: The Extract and Load Data Pipeline Pattern

• 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
• Lab: BigLake: Qwik Start
• Quiz

Module 04: The Extract, Load, and Transform Data Pipeline Pattern

• 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.
• Lab: Create and Execute a SQL Workflow in Dataform
• Quiz

Module 05: The Extract, Transform, and Load Data Pipeline Pattern

• 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.
• 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 06: Automation Techniques

• 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.
• Lab: Use Cloud Run Functions to Load BigQuery (optional for ILT)
• Quiz

• Prior Google Cloud experience at the fundamental level using Cloud Shell and accessing products from the Google Cloud console.
• Basic proficiency with a common query language such as SQL.
• Experience with data modeling and ETL (extract, transform, load) activities.
• Experience developing applications using a common programming language such as Python

None

No recommendation

• Official course book provided to participants