Codeless Data Engineering in GCP: Beginner to Advanced

Codeless Data Engineering in GCP: Beginner to Advanced. Step by step guide to building four data pipelines in Google Cloud using DataStream, Data Fusion, DataPrep, DataFlow, etc.

In this course, we will be creating a data lake using Google Cloud Storage and bringing data warehouse capabilities 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.

Codeless Data Engineering in GCP

The course will follow a logical progression of 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.

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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
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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

What you’ll learn

  • How to build No Code/Codeless data pipelines in Google Cloud
  • You will learn to build real-world data pipelines usings tools like Data Fusion, DataPrep and Dataflow
  • You will learn to transform data using Data Fusion
  • You will acquire good data engineering skills in Google Cloud
  • Working with Big Query Data warehouse in Google Cloud
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Who this course is for:

  • Data Engineers
  • Data Architects looking to architect data integration solutions in Google Cloud
  • Data Scientist, Data Analyts and Database Administrators
  • Data Scientist, Data Analyts and Database Administrators
  • Anyone looking to start a career as an Google Cloud Data Engineer

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