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Data Engineering with Google Cloud Platform

Data Engineering with Google Cloud Platform

By : Adi Wijaya
4.7 (12)
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Data Engineering with Google Cloud Platform

Data Engineering with Google Cloud Platform

4.7 (12)
By: Adi Wijaya

Overview of this book

With this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.
Table of Contents (17 chapters)
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1
Section 1: Getting Started with Data Engineering with GCP
4
Section 2: Building Solutions with GCP Components
11
Section 3: Key Strategies for Architecting Top-Notch Data Pipelines

Summary

In this chapter, we learned about Cloud Composer. Having learned about Cloud Composer, we then needed to know how to work with Airflow. We realized that as an open source tool, Airflow has a wide range of features. We focused on how to use Airflow to help us build a data pipeline for our BigQuery data warehouse. There are a lot more features and capabilities in Airflow that are not covered in this book. You can always expand your skills in this area, but you will already have a good foundation after finishing this chapter.

As a tool, Airflow is fairly simple. You just need to know how to write a Python script to define DAGs. We've learned in the Level 1 DAG exercise that you just need to write simple code to build your first DAG, but a complication arises when it comes to best practices, as there are a lot of best practices that you can follow. At the same time, there are also a lot of potential bad practices that Airflow developers can make.

By learning the examples...

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