Practical Data Engineering in GCP: Beginner to Advanced

What you'll learn:

Codeless Data Engineering in GCP: Beginner to Advanced

Requirements:

• A general understanding of cloud computing.
  A Google account that is still in use.
  A basic understanding of what a data lake and a data warehouse are is important, but it’s not a must.

Description:

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

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
• Mastering Numpy,Pandas and MatplotLib-Data Manipulation Tool
• Building Design System in Figma from Scratch - UI UX Mastery
• Image Super-Resolution GANs
• Complete Guide to Redux-Toolkit & RTK Query with React JS

Course Details:

• 4.5 hours on-demand video
• 2 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of completion

Practical Data Engineering in GCP: Beginner to Advanced udemy courses free download

Step by step guide to building four data pipelines in Google Cloud using DataStream, Data Fusion, DataPrep, DataFlow etc