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Fundamentals of Data Engineering: Plan and Build Robust Data Systems
DKK 633
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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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Hvad der skiller sig ud
Produktdetaljer
- Comprehensive view of data engineering lifecycle and best practices
- Learn to plan and build systems using cloud technologies
- Understand data generation, ingestion, orchestration, transformation, storage, and governance
- Cut through marketing hype when choosing data technologies and architecture
- Incorporate data governance and security across the data engineering lifecycle
- Target audience includes software engineers, data scientists, data analysts, and technical practitioners
| Publisher | O'Reilly Media |
| Publication date | June 22, 2022 |
| Edition | 1st |
| Language | English |
| File size | 6.4 MB |
| Enhanced typesetting | Enabled |
| X-Ray | Not Enabled |
| Word Wise | Not Enabled |
| Print length | 747 pages |
| ISBN-13 | 978-1098108267 |
| Page Flip | Enabled |
| Item Weight | 1 lbs (450 grams) |
Hvem bør købe?
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Aspiring Data Engineers
Perfect for beginners looking to understand data system design and implementation basics for their future careers.
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Technical Managers
Useful for managers overseeing data projects, helping them understand fundamental concepts for better team leadership.
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Data Enthusiasts
Ideal for individuals passionate about data who want to deepen their understanding of engineering methodologies and best practices.
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Experienced Professionals
May not be suitable for seasoned engineers seeking advanced topics beyond foundational data engineering concepts.
PRODUKTBESKRIVELSE
Fundamentals of Data Engineering: Plan and Build Robust Data Systems
Kundespørgsmal & svar
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spørgsmål:
What is the main focus of 'Fundamentals of Data Engineering'?
svar: The book primarily focuses on providing a comprehensive foundation in data engineering principles. It covers essential topics such as data architecture, scalable data processing, and data pipeline design, which are critical for creating robust data systems. This resource is ideal for professionals looking to deepen their understanding of data workflows and system integration in real-world scenarios. -
spørgsmål:
Who is the target audience for this book?
svar: The target audience includes aspiring data engineers, data scientists, and IT professionals who want to enhance their skills in data management. Additionally, it benefits students and industry practitioners interested in understanding the complexities of working with large data infrastructures, making it a valuable resource for various expertise levels. -
spørgsmål:
What are the key topics covered in this book?
svar: Key topics include data modeling, ETL (Extract, Transform, Load) processes, cloud data solutions, and best practices for database management. Each chapter provides practical insights that enable readers to apply theoretical concepts to real-world data challenges, creating a practical guide for building effective data systems. -
spørgsmål:
How can this book help in preparing for a career in data engineering?
svar: This book serves as a practical guide for those preparing for a career in data engineering. It equips readers with fundamental knowledge and skills necessary to tackle real-life data challenges. By providing case studies and practical examples, it enhances problem-solving abilities that are crucial for various roles across the technology industry. -
spørgsmål:
Are there exercises or practical examples included?
svar: Yes, the book includes various exercises and examples designed to reinforce learning. These hands-on scenarios encourage readers to apply what they've learned in each chapter, helping solidify their understanding of data engineering principles through practical implementation, which is essential for real-world application. -
spørgsmål:
Is this book suitable for beginners with no prior experience in data engineering?
svar: Absolutely. 'Fundamentals of Data Engineering' is structured to cater to readers with no prior experience, gradually introducing core concepts. Its accessible language and clear explanations ensure that even novices can grasp essential data engineering principles, making it an excellent starting point for their journey into this field. -
spørgsmål:
What programming languages or tools are discussed in the book?
svar: The book discusses various programming languages and tools commonly used in data engineering, such as Python, SQL, and popular data processing frameworks like Apache Spark. Familiarity with these tools can significantly enhance a reader's ability to implement the concepts discussed, making it crucial for modern data workflows. -
spørgsmål:
How does this book address the importance of data quality and governance?
svar: The book places a strong emphasis on data quality and governance, exploring how to ensure the accuracy and reliability of data systems. Sections dedicated to best practices and strategies for maintaining data integrity help readers understand the criticality of implementing robust data governance frameworks within organizations. -
spørgsmål:
In what ways can this book assist organizations in implementing data systems?
svar: This book provides organizations with a structured approach to designing and implementing data systems. By offering frameworks and methodologies, it guides teams in building efficient data pipelines and infrastructures that support data-driven decision-making, improving overall organizational performance and adaptability. -
spørgsmål:
Where can I buy 'Fundamentals of Data Engineering: Plan and Build Robust Data Systems' in Greenland?
svar: You can purchase 'Fundamentals of Data Engineering: Plan and Build Robust Data Systems' from Ubuy, which provides a convenient platform for accessing this valuable resource. Ubuy often features competitive offers and ensures a seamless shopping experience tailored to your needs in Greenland.
Data Mining Editorial Review
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Egenskaber og fordele
- 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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