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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

Tips for optimizing BigQuery using partitioned and clustered tables 

BigQuery tables can store data from zero bytes to petabytes of data. There will be no difference between creating a small-sized table or a large-sized table. To simplify the context and for illustration purposes only, let's say a small-sized table ranges from KBs to 100 GB. The large-sized tables range from 100 GB to PBs of data. Technically, both tables are the same, but if you think about optimizing performance and cost, we can configure the tables using two features called BigQuery partitioned table and BigQuery clustered table

These features are helpful for both on-demand and flat-rate pricing. In the on-demand pricing, the features will cut the billed bytes and will reduce the overall cost that is calculated from the billed bytes. With flat-rate pricing, it doesn't affect it directly. Remember that the cost of flat-rate pricing is flat per period. But when you're using features,...

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