Hands-On Data Engineering in Google Cloud Platform | Python

Non-stop hands-on videos on serverless and self-managed technology stacks within GCP for aspiring data professionals!

You are going to learn about how we can create data engineering solutions in Google Cloud Platform (GCP) using different available tools, mainly using Python as our core programming language. We will start from basic introduction of what each component is mainly used for, then dive straight into demos with detailed explanation on the design choice and reasonings behind. I am confident that you will come out of the course with a better understanding on what each component could possibly do and drive data engineering solutions in your team!

What you’ll learn

  • Extract and load data from local machine to Cloud Storage and Pub/Sub..
  • Setup and use database such as Cloud SQL, BigQuery and Bigtable..
  • Transform data using Google Cloud Functions, Dataflow and Dataproc..
  • Implement real life end-to-end solutions by combining latest available tools in Google Cloud Platform..

Course Content

  • Introduction –> 2 lectures • 2min.
  • Extract –> 7 lectures • 1hr 15min.
  • Load –> 3 lectures • 25min.
  • Transform – Cloud Functions –> 5 lectures • 1hr 9min.
  • Transform – Cloud Dataflow –> 6 lectures • 1hr 14min.
  • Transform – Cloud Dataproc –> 7 lectures • 1hr 47min.
  • Summary and Thank You Note –> 1 lecture • 2min.

Hands-On Data Engineering in Google Cloud Platform | Python

Requirements

  • Basic Python, docker and bash knowledge. Detailed steps will be provided in both video and self-written articles by author, so no worries if you are a totally beginner!.
  • Sign up for 90-day $300 Google Cloud Free Trial – credit card/payment method required. Google won’t charge you unless you explicitly upgrade from your free trial to a paid account, so no worries!.
  • Stable internet connection to watch all the non-stop live demos in 1080p!.

You are going to learn about how we can create data engineering solutions in Google Cloud Platform (GCP) using different available tools, mainly using Python as our core programming language. We will start from basic introduction of what each component is mainly used for, then dive straight into demos with detailed explanation on the design choice and reasonings behind. I am confident that you will come out of the course with a better understanding on what each component could possibly do and drive data engineering solutions in your team!

 

There are the list of tech stacks that we will be covering

  1. Integrating with GCP
    1. Google Cloud Platform Console, Cloud SDK and Client Libraries
  2. Data Storage/ Messaging
    1. Google Cloud Storage, Google Pub/Sub
  3. Databases
    1. Cloud SQL, BigQuery, Bigtable
  4. Data Processing
    1. Cloud Functions, Cloud Dataflow, Cloud Dataproc

I have created a few specialised labs to integrate all the tools mentioned above instead of telling you what each component could do! It could help to accelerate your understanding so you can apply on your existing GCP workflow immediately. Detailed code and steps will also be shared in the video as well as self-written articles, so just sit back, relax and enjoy all the demos!

 

Let’s get onboard on the cloud journey now!