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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

A quick look at machine learning

First, let's understand what ML is from a Data engineering perspective. ML is a data process that uses data as input. The output of the process is a generalized formula for one specific objective. 

For better illustration, let's imagine some of the real-world use cases that use ML. The first example is a recommendation system from an e-commerce platform. This eCommerce platform may use ML to use the customer's purchase history as input data. This data can be processed to calculate how likely each customer will purchase other items in the future. Another example is a cancer predictor that uses X-ray images from the health industry. A collection of X-ray images with cancer and without cancer can be used as input data and be used to predict unidentified X-ray images.

I believe you've heard about those kinds of ML use cases and many other real-world use cases. For data engineers, it's important to notice that ML is not...

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