: Practical Data Engineering in GCP: Beginner to Advanced
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 2.19 GB | Duration: 4h 37m
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 eeering skills in Google Cloud
Working with Big Query Data warehouse in Google Cloud
Requirements
Basic understanding of cloud computing
An active google account
basic understanding of what a data lake and data warehouse are is essential but not required
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 transfog 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 perfog 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 tranfog 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 perfog 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 real data pipeline for streaming messages to BigQuery
Chapter 5 - Data Pipeline 4
Introduction to Cloud DataPrep
Profile, Author and monitor ETL jobs for tranfog our data using DataPrep
Who this course is for
Data Eeers
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 Eeer
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