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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

Summary

As a summary of the first chapter, we've learned the fundamental knowledge we need as data engineers. Here are some key takeaways from this chapter. First, data doesn't stay in one place. Data moves from one place to another, called the data life cycle. We also understand that data in a big organization is mostly in silos, and we can solve these data silos using the concepts of a data warehouse and data lake.

As someone who has started to look into data engineer roles, you may be a little bit lost. The role of data engineers may vary. The key takeaway is not to be confused about the broad expectation in the market. First, you should focus on the core and then expand as you get more and more experience from the core. In this chapter, we've learned what the core for a data engineer is. At the end of the chapter, we learned some of the key concepts. There are three key concepts as a data engineer that you need to be familiar with. These concepts are ETL, big data, and distributed systems

In the next chapter, we will visit GCP, a cloud platform provided by Google that has a lot of services to help us as data engineers. We want to understand its preposition and what the services are that are relevant to big data, and lastly, we will start using the GCP console.

Now let's put the knowledge from this chapter into practice.

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