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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

Understanding how Data Studio can impact the cost of BigQuery

At a very high level, the total BigQuery cost is driven by how big your data is and the amount of usage. Both factors work as multipliers. For example, if you have a table that's 1 TB in size and you access the table 10,000 times in a month, it means the BigQuery cost will be 1 TB x 10,000 x $5 = $50,000 / month.

Whether $50,000 is expensive or not depends on your organization. But we will ignore the context and focus on the cost driver aspects, so let's say $50,000 is expensive. Now the questions are what kind of table could be 1 TB in size? and how can a table be accessed 10,000 times in a month? Let's discuss these questions in the following sections.

What kind of table could be 1 TB in size?

To answer that, let's take a look at our data warehouse diagram from Chapter 3, Building a Data Warehouse in BigQuery, in the following figure:

Figure 7.25 – High-level data...

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