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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

Introduction to Google Cloud Storage and BigQuery

Google Cloud Storage (GCS) is object storage. It's a service that is fully managed by GCP, which means we don't need to think about any underlying infrastructure for GCS. For example, we don't need to think about pre-sizing the storage, the network bandwidth, number of nodes, or any other infrastructure-related stuff.

What is object storage? Object storage is a highly scalable data storage architecture that can store very large amounts of data in any format. 

Because the technology can store data in almost any size and format, GCS is often used by developers to store any large files, for example, images, videos, and large CSV data. But, from the data engineering perspective, we will often use GCS for storing files, for example, as dump storage from databases, for exporting historical data from BigQuery, for storing machine learning model files, and for any other purpose related to storing files. 

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