Practical Data Engineering in GCP: Beginner to Advanced

About 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