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Codeless Data Engineering in GCP: Beginner to Advanced

Codeless Data Engineering in GCP: Beginner to Advanced
Codeless Data Engineering in GCP: Beginner to Advanced

Codeless Data Engineering in GCP: Beginner to Advanced

Use these tools to build four data pipelines in the Google Cloud. This is a step-by-step tutorial on how to use them to build them.

What you’ll learn

Codeless Data Engineering in GCP: Beginner to Advanced

  • How to make data pipelines in Google Cloud that don’t use any code.
  • Use tools like Data Fusion, DataPrep, and Dataflow to build real-world data pipelines that can be used in the real world.
  • You will learn how to use Data Fusion to change data.
  • In Google Cloud, you will learn how to do good data engineering.
  • With Big Query Data warehouse in Google Cloud, you can work with it

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

Google Cloud Storage will be used in this course to make a data lake. We will also use Google BigQuery to bring data warehouse capabilities to the data lake to make the lakehouse architecture. Using services like DataStream, Dataflow, DataPrep, Pub/Sub and Cloud Storage as well as BigQuery, we will build four no-code data pipelines that will send and receive data.

Students will learn how to set up a data lake, build data pipelines for data ingestion, and transform data for analytics and reporting in a way that makes sense to them.

This is the first chapter of the course.

  • We’ll start a project in Google Cloud.
  • It’s time to learn about Google Cloud Storage.
  • In this video, we’re going to show you how to use Google BigQuery

Data Pipeline 1

  • Before we do any complicated ETL jobs, we will set up a cloud SQL database and add data to it.
  • It is important for us to use DataStream Change Data Capture to stream data from our Cloud SQL Database into our data lake built with Cloud Storage.
  • This is what we need to do: Add a notification to our bucket for people to see.
  • Create a Dataflow Pipeline so that jobs can be streamed into BigQuery.

Data Pipeline 2

  • Introduce Google’s Data Fusion tool.
  • An ETL job is a way to change data and move it to a new place in our data lake. You write and watch the job to make sure it works.
  • Data must be cleaned and normalized before it can be used in a study.
  • Using metadata in Data Fusion, you can find and keep track of your data.

Data Pipeline 3

  • In this video, we’ll show you how to use Google Pub/Sub.
  • Then, I’ll make a .Net app that will send data to a Pub/Sub topic.
  • Creating a real-time data pipeline to send messages to BigQuery as they come in

Data Pipeline 4

  • Getting Started with Cloud Data Prep
  • Profile, write and keep an eye on ETL jobs that use DataPrep to change our data.

Who this course is for:

  • A data engineer is a person who works with data.
  • Data architects who want to design data integration solutions in the Google Cloud.
  • Data Scientists, Data Analysts, and Database Administrators work together.
  • The Data Scientists, Data Analysts, and Database Administrators work together.
  • Anyone who wants to work for Google as a Cloud Data Engineer.

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