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Packt is a leading publisher of technical learning content with the ability to publish books on emerging tech faster than any other.

Our mission is to increase the shared value of deep tech knowledge by helping tech pros put software to work.

We help the most interesting minds and ground-breaking creators on the planet distill and share the working knowledge of their peers.

Publisher ‏ : ‎ Packt Publishing; 2nd ed. edition (April 30, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 476 pages
ISBN-10 ‏ : ‎ 1835080111
ISBN-13 ‏ : ‎ 978-1835080115
Item Weight ‏ : ‎ 1.8 pounds
Dimensions ‏ : ‎ 0.99 x 7.5 x 9.25 inches

Data engineering is a crucial aspect of any data-driven organization, and with Google Cloud Platform (GCP), data engineers have access to a wide range of powerful tools and services to build scalable and reliable data platforms. In this guide, we will explore how data engineers can level up their skills by leveraging GCP to build a scalable data platform.

1. Understanding the basics of Google Cloud Platform
Before diving into building a data platform with GCP, it is essential to have a solid understanding of the platform’s core services and concepts. This includes services like BigQuery, Cloud Storage, Dataflow, and Pub/Sub, as well as concepts like data ingestion, processing, and storage.

2. Designing a scalable data platform architecture
The key to building a scalable data platform is designing an architecture that can handle large volumes of data and growing demands. With GCP, data engineers can leverage services like BigQuery for querying massive datasets, Dataflow for real-time data processing, and Cloud Storage for storing large amounts of data.

3. Data ingestion and processing
One of the first steps in building a data platform is ingesting data from various sources and processing it to make it usable for analysis. GCP provides tools like Dataflow and Pub/Sub for real-time data ingestion and processing, as well as tools like Dataprep for cleaning and transforming data.

4. Data storage and warehousing
Once data has been ingested and processed, it needs to be stored in a reliable and scalable manner. GCP offers services like BigQuery for data warehousing, Cloud Storage for object storage, and Cloud SQL for relational databases, providing data engineers with a range of options for storing and querying data.

5. Building data pipelines
Data pipelines are essential for automating data processing and analysis workflows. With GCP’s Dataflow service, data engineers can build scalable and fault-tolerant pipelines that can process data in real-time or batch mode, enabling them to extract insights from large volumes of data quickly and efficiently.

By following these steps and leveraging the power of Google Cloud Platform, data engineers can level up their skills and build a scalable data platform that can handle the demands of modern data-driven organizations. With GCP’s powerful tools and services, data engineers can unlock new possibilities for data analysis and insights, enabling their organizations to make better-informed decisions and drive business growth.
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