Practical Data Engineering in GCP: Beginner to Advanced

In this course, we will be creating a data lake using Google Cloud Storage and bring data warehouse capabilites to the data lake to form the lakehouse architecture using Google BigQuery. We will be building four no code data pipelines using services such as DataStream, Dataflow, DataPrep, Pub/Sub, Data Fusion, Cloud Storage, BigQuery etc.

The course will follow a logical progression of a real world project implementation with hands on experience of setting up  a data lake,  creating data pipelines  for ingestion and transforming your data in preparation for analytics and reporting.

Chapter 1

• We will setup a project in Google Cloud

• Introduction to Google Cloud Storage

• Introduction to Google BigQuery

Chapter 2 - Data Pipeline 1

• We will create a cloud SQL database and populate with data before we start performing complex ETL jobs.

• Use DataStream Change Data Capture for streaming data from our Cloud SQL Database into our Data lake built with Cloud Storage

• Add a pub/sub notification to our bucket

• Create a Dataflow Pipeline for streaming jobs into BigQuery

Chapter 3 - Data Pipeline 2

• Introduce Google Data Fusion

• Author and monitor ETL jobs for tranforming our data and moving them  between different zone of our data lake

• We will explore the use of Wrangler in Data Fusion for profiling and understanding our data before we starting performing complex ETL jobs.

• Clean and normalise data

• Discover and govern data using metadata in Data Fusion

Chapter 4 - Data Pipeline 3

• Introduction to Google Pub/Sub

• Building a .Net application for publishing data to a Pub/Sub topic

• Building a realtime data pipeline for streaming messages to BigQuery

Chapter 5 - Data Pipeline 4

• Introduction to Cloud DataPrep

• Profile, Author and monitor ETL jobs for tranforming our data using DataPrep