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 – deploying a dummy workflow with Vertex AI Pipeline

Before we continue with the hands-on exercise, let's understand what Vertex AI Pipeline is. Vertex AI Pipeline is a tool for orchestrating ML workflows. Under the hood, it uses an open source tool called Kubeflow Pipeline. Similar to the relationship between Airflow and Cloud Composer or Hadoop and DataProc, to understand Vertex AI Pipeline, we need to be familiar with Kubeflow Pipelines.

Kubeflow Pipeline is a platform for building and deploying portable, scalable ML workflows based on Docker containers. Using containers for ML workflows is very important compared to data workflows. For example, in data workflows, it's typical to load BigQuery, GCS, and pandas libraries for all the steps. Those libraries will be used in both the upstream and downstream steps. In ML, the upstream is data loading; the other step is building models that need specific libraries such as TensorFlow or scikit-learn, while the...