Book Image

Data Engineering with Google Cloud Platform

By : Adi Wijaya
3 (1)
Book Image

Data Engineering with Google Cloud Platform

3 (1)
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)
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

Exercise – leveraging pre-built GCP models as a service 

In this exercise, we want to use a GCP service called Google Cloud Vision. Google Cloud Vision is one of many pre-built models in GCP. In pre-built models, we only need to call the API from our application. This means that we don't need to create an ML model. 

In this exercise, we will create a Python application that can read an image with handwritten text and convert it into a Python string. 

The following are the steps for this exercise:

  1. Upload the image to a GCS bucket.
  2. Install the required Python packages.
  3. Create a detect text function in Python.

Let's start by uploading the image.

Uploading the image to a GCS bucket

In the GCS console, go to the bucket that you created in the previous chapters. For example, my bucket is packt-data-eng-on-gcp-data-bucket.

Inside the bucket, create a new folder called chapter-8. This is an example from my console:

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