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57565926
Data Engineering with Google Cloud Platform: A practical guide to operationalizing scalable data analytics systems on GC, (Paperback)
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Nº de artículo:
57565926
USD 88
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Gain the confidence to boost your career as a data engineer with this comprehensive guide to operationalizing scalable data analytics systems on GCP.
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What Stands Out
Practical Approach
Offers hands-on techniques for building and managing data analytics on Google Cloud, ensuring readers can apply learned concepts directly to real-world scenarios.
Scalability Focus
Emphasizes scalable solutions, helping users understand how to efficiently manage growing data needs without compromising on performance and reliability.
Comprehensive Guide
Covers a wide range of topics related to data engineering on GCP, making it a valuable resource for both beginners and experienced professionals looking to enhance their skills.
Detalles de producto
- Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineerKey Features: Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solutionLearn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelinesDiscover tips to prepare for and pass the Professional Data Engineer examBook Description: 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.What You Will Learn: Load data into BigQuery and materialize its output for downstream consumptionBuild data pipeline orchestration using Cloud ComposerDevelop Airflow jobs to orchestrate and automate a data warehouseBuild a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc clusterLeverage Pub/Sub for messaging and ingestion for event-driven systemsUse Dataflow to perform ETL on streaming dataUnlock the power of your data with Data StudioCalculate the GCP cost estimation for your end-to-end data solutionsWho this book is for: This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | March, 2022 |
| Pages | 440 |
| Subgenre | Data Science |
| Series title | No Series |
| Edition | 1 |
| Publisher | Packt Publishing |
| Original languages | English |
| Language | English |
| Edu focus | Engineering |
| Educational level | General |
| Is collectible | N |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 7.50 x 0.89 x 9.25 in (19.1 x 2.3 x 23.5 cm) |
| Assembled product weight | 1.66 lb (750 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Data Engineers
Professionals who need to design and implement end-to-end data processing workflows on Google Cloud Platform.
-
Cloud Architects
Individuals looking to architect robust data analytics solutions utilizing Google Cloud services and best practices.
-
Students/Graduates
Learners aiming to gain practical skills in data engineering using Google Cloud through hands-on guidance in the book.
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Beginner Programmers
Users with no prior knowledge of programming or data engineering concepts may find this book challenging.
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Adi Wijaya All Books Editorial Review
Data Engineering with Google Cloud Platform: A practical guide to operationalizing scalable data analytics systems on GC is an essential read for professionals in the computing and internet domain, specifically tailored for data science enthusiasts. This paperback edition, published in March 2022, spans 440 pages and provides a comprehensive insight into engineering concepts. Readers appreciate the book for its hands-on approach to implementing scalable data analytics systems, which aids in real-world applications. Furthermore, it is written in English and serves as a significant educational resource for individuals looking to enhance their technical skills in data engineering.
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ventajas
- Comprehensive hands-on approach to data analytics
- Excellent resource for data engineering professionals
- Covers scalable analytics systems effectively
- Suitable for general educational levels
- Recent publication with up-to-date content
Contras
- Some readers may seek additional advanced topics.
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características y beneficios
- Understand key data engineering concepts and the role of a data engineer.
- Learn how to leverage Google Cloud Platform (GCP) for building scalable data solutions.
- Develop skills in data ingestion, transformation, and workflow orchestration.
- Get practical guidance for passing the Professional Data Engineer certification exam.
- Build a data warehouse and data lake using GCP products like BigQuery and Dataproc.
- Unlock the power of your data with visualization tools and effective reporting.
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