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Fundamentals of Data Engineering: Plan and Build Robust Data Systems
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Learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.
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Detalji o proizvodu
- Learn how to plan and build robust data systems through the data engineering lifecycle
- Evaluate the best technologies available for data engineering
- Understand the concepts of data generation, ingestion, orchestration, transformation, storage, and governance
- Cut through marketing hype to make informed decisions on data technologies, architecture, and processes
- Design and build a robust architecture using the data engineering lifecycle
- Incorporate data governance and security throughout the data engineering lifecycle
| Publisher | O'Reilly Media |
| Publication date | July 26, 2022 |
| Edition | 1st |
| Language | English |
| Print length | 447 pages |
| ISBN-10 | 1098108302 |
| ISBN-13 | 978-1098108304 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 7 x 1 x 9.25 inches (17.8 x 2.5 x 23.5 cm) |
Who Should Buy?
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Aspiring Data Engineers
This book provides foundational knowledge crucial for those starting their career in data engineering.
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Data Professionals
Professionals looking to enhance their skills in building and managing data systems will find valuable insights.
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Project Managers
Managers overseeing data projects can benefit from understanding data engineering principles and best practices.
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Beginners in Tech
Complete beginners may find the content challenging without prior knowledge of data concepts and engineering.
OPIS PROIZVODA
Fundamentals of Data Engineering: Plan and Build Robust Data Systems
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Data Modeling & Design Editorial Review
"Fundamentals of Data Engineering: Plan and Build Robust Data Systems 1st Edition" is a book that aims to present a full picture of the whole data science pipeline through the data engineering lens. However, some customers found the book unnecessarily long and unengaging in its presentation. They also criticized the authors' lack of discussion about how data science, particularly in its modeling and algorithm sense, fits into data engineering. Despite these criticisms, some customers found the book well-organized, easy to read, and with many insights in every chapter. They also praised the authors' conversational tone and their ability to provide a good balance between the main framework of data engineering and new concepts in the field.
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Pros
- Well-organized
- Easy to read
- Provides insights in every chapter
- Presents a good balance between the main framework of data engineering and new concepts in the field
- Written in a conversational tone
Protiv
- Some customers found the book unnecessarily long and unengaging in its presentation
Product Price History
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Značajke i prednosti
- Understand the data engineering lifecycle
- Assess data engineering problems using best practices
- Choose the right data technologies, architecture, and processes
- Design and build a robust architecture
- Incorporate data governance and security
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