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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

Exercise – deploying a scikit-learn model pipeline with Vertex AI

In this exercise, we will simulate creating a pipeline for an ML model. There will be two pipelines – one to train the ML model and another to predict new data using the model from the first pipeline. We will continue using the Credit Card Default dataset. The two pipelines will look like this:

Figure 8.22 – Steps in the two pipelines

Later in this section, we will load data from BigQuery. But instead of storing the data in pandas, we will write the output to a GCS bucket. We will be doing this as we don't want to return an in-memory Python object from the function. What I mean by an in-memory Python object, in this case, is a pandas DataFrame. This also applies to other data structures, such as arrays or lists. Remember that every step in Vertex AI Pipeline will be executed in a different container – that is, in a different machine. You can't pass the...

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