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32821570
Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python
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товар №:
32821570
€ 52
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What Stands Out
Data Model Design
Create efficient data models tailored to specific business needs, enhancing data storage and retrieval processes for optimal performance in data-heavy environments.
Pipeline Automation
Automate complex data workflows, reducing manual errors and increasing data processing speed, which allows teams to focus on analysis rather than repetitive tasks.
Comprehensive Learning
Offers in-depth coverage of Python tools and techniques for data engineering, empowering users to build robust data solutions with real-world applications.
Информация о продукте
| Publisher | Packt Publishing |
| Publication date | October 23, 2020 |
| Language | English |
| Print length | 356 pages |
| ISBN-10 | 183921418X |
| ISBN-13 | 978-1839214189 |
| Item Weight | 1.35 pounds (610 grams) |
| Dimensions | 7.5 x 0.81 x 9.25 inches (19.1 x 2.1 x 23.5 cm) |
Who Should Buy?
-
Aspiring Data Engineers
Ideal for individuals looking to build foundational knowledge in data engineering concepts using Python effectively.
-
Developers Transitioning
Great for software developers wanting to transition into data engineering roles by learning specific data management practices.
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Data Analysts Upgrading Skills
Perfect for data analysts wanting to deepen their understanding of data modeling and pipeline automation using Python.
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Beginner Programmers
Not suitable for those with no programming background, as it requires a solid understanding of Python and data concepts.
ОПИСАНИЕ ТОВАРА
Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python
About This Item
Take your data engineering skills to the next level with Data Engineering with Python. This comprehensive guide will equip you with the knowledge and techniques necessary to work with massive datasets, design efficient data models, and automate complex data pipelines using the power of Python. With the increasing volume and complexity of data in today's world, data engineers play a crucial role in organizing and transforming raw data into valuable insights. This book will teach you how to leverage the flexibility and scalability of Python to streamline your data engineering workflows and unlock the potential of your data. Whether you are a seasoned data engineer looking to enhance your skills or a beginner eager to dive into the world of data engineering, this book is your ultimate resource.
You will learn how to build robust data models that can handle large volumes of data and adapt to changing requirements. This includes techniques for data cleaning, data transformation, and data integration using Python. Automation is a key aspect of modern data engineering, and this book will guide you through the process of automating data pipelines with Python. You will learn how to schedule and orchestrate data pipelines, ensure data quality and reliability, and monitor and troubleshoot your workflows. In addition, this book covers best practices for Python data engineering, providing guidance on how to optimize performance, ensure scalability, and maintain code quality.
It also introduces a range of useful Python libraries and frameworks specifically designed for data engineering tasks, such as Apache Airflow, Pandas, and SQLAlchemy. To help you apply your newly acquired skills in real-world scenarios, this book includes hands-on projects and tutorials. You will explore various data engineering use cases and tackle practical challenges using Python. Whether you are working with structured or unstructured data, Data Engineering with Python will empower you to tackle complex data engineering tasks with confidence and efficiency. Get started on your data engineering journey today and unlock the full potential of your data with Python.
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Data Modeling & Design Editorial Review
Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python is a comprehensive guide that provides a solid overview of pipelining and database connections, particularly useful for those working with batch and stream data flows. Readers appreciate the many practical examples featuring tools like Pandas, Kafka, Spark, and NiFi, making it a valuable resource even in a CI/CD environment. However, it's noted that the book could benefit from better explanations about specific tools and the Python versions used. Overall, it's a good addition for anyone looking to bridge the gap into data engineering, despite some outdated content.
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Плюсы
- Solid explanations of data pipelining concepts
- Includes practical examples of popular tools
- Helpful for beginners learning data engineering
- Illustrative content enhances understanding
- Useful in CI/CD environments with modern tools
Минусы
- Some content feels outdated and could be improved
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Особенности и преимущества
- Learn data architectures and how to prepare and optimize data using Python
- Understand ETL and building data pipelines to work with large datasets
- Transform and analyze data to gain insights
- Build and deploy production-ready data pipelines
- Ideal for data analysts, ETL developers, and those looking to transition to data engineering or advance their skills
- No previous knowledge of data engineering required
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