Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python
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Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python
This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python.
Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python
Numéro d'article: 32821570

Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python

85% des répondants recommanderaient ceci à un ami

Numéro d'article: 32821570

XOF 38517

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Ce qui se démarque

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.

Détails du produit

Shop Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python online at a best price in Senegal. 183921418X
  • Build, monitor, and manage real-time data pipelines using Python
  • Gain practical experience in data architectures, data preparation, and data optimization
  • Learn how to design data models and perform ETL (Extract, Transform, Load) using Python
  • Schedule, automate, and monitor complex data pipelines in production
  • Explore various tools and methods used in data engineering
  • Build data engineering pipelines for tracking, quality checks, and production changes
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)

À qui est-ce destiné ?

Suitable For
  • 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.

  • Data Analysts Upgrading Skills

    Perfect for data analysts wanting to deepen their understanding of data modeling and pipeline automation using Python.

Not Suitable For
  • Beginner Programmers

    Not suitable for those with no programming background, as it requires a solid understanding of Python and data concepts.

DESCRIPTION DU PRODUIT

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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Questions et réponses des clients

  • question: What is covered in the book 'Data Engineering with Python'?

    répondre: The book covers essential concepts and techniques in data engineering using Python, including designing data models, handling massive datasets, and automating data pipelines. It delves into data ingestion, transformation, and storage, integrating libraries like Pandas and NumPy. The practical examples provided help you apply these techniques in real-world scenarios, making it suitable for both beginners and seasoned professionals in the data field.
  • question: Who is the target audience for 'Data Engineering with Python'?

    répondre: This book targets data engineers, data analysts, and software developers who wish to enhance their skills in data processing. It is also beneficial for students and professionals looking to transition into the data engineering field. Through hands-on projects and practical examples, readers can familiarize themselves with applicable tools and frameworks used in contemporary data engineering roles.
  • question: What skills can I expect to gain by studying 'Data Engineering with Python'?

    répondre: By studying this book, you can gain a robust understanding of data modeling, data pipeline automation, and working with large datasets. You’ll learn to utilize Python libraries effectively for data manipulation and analysis. These skills are invaluable for leveraging data-driven insights within organizations, enabling you to contribute to data-centric projects and make informed data decisions.
  • question: Can beginners benefit from 'Data Engineering with Python'?

    répondre: Absolutely! The book is structured to cater to a wide range of skill levels, making it accessible to beginners as well. It starts with foundational concepts before progressing to more complex topics. With practical examples and clear explanations, beginners can gradually build their confidence and capabilities in data engineering, preparing them for more advanced studies or career opportunities in the field.
  • question: How does this book help with automating data pipelines?

    répondre: The book emphasizes automation by introducing techniques in Python tailored for creating efficient data pipelines. It covers topics such as scheduling data processing tasks and integrating various data sources seamlessly. By implementing automation, you can handle recurring data tasks with reduced manual effort, enhancing productivity and ensuring data is processed in a timely and systematic manner.
  • question: What are the prerequisites for understanding 'Data Engineering with Python'?

    répondre: While there are no strict prerequisites, a basic understanding of Python programming and familiarity with data concepts can greatly enhance your comprehension. Knowledge of database systems and queries can also provide a strong foundation. The book builds on these basics, helping readers connect existing knowledge with new data engineering skills and practices.
  • question: Can this book be used for practical projects?

    répondre: Certainly! 'Data Engineering with Python' is rich with practical projects and real-world scenarios that allow readers to apply their learning in a hands-on way. Projects included range from setting up data pipelines to executing complex data transformations. These exercises are designed to reinforce the theoretical aspects of the book, ensuring a comprehensive learning experience.
  • question: How does this book address handling massive datasets?

    répondre: The book provides in-depth discussions and methodologies for effectively managing massive datasets, including techniques for data optimization and storage. It explores using Python's capabilities with big data technologies as well, such as Hadoop and Spark. By mastering these strategies, readers can efficiently analyze and extract insights from large data volumes, crucial for modern data engineering tasks.
  • question: What makes 'Data Engineering with Python' stand out from other data engineering books?

    répondre: What sets this book apart is its practical approach and focus on Python as a primary tool. While many resources touch on data engineering concepts, this book provides comprehensive projects and real-world applications, making the learning experience more engaging and applicable. The hands-on style ensures that readers not only understand the theory but can also implement their skills practically.
  • question: Where can I buy 'Data Engineering with Python'?

    répondre: You can purchase 'Data Engineering with Python' on Ubuy in Senegal. Ubuy offers a wide selection of books and ensures a smooth shopping experience, making it easier for readers to access resources that can enhance their data engineering skills and knowledge.

Data Modeling & Design Editorial Review

The book "Data Engineering with Python" has received mixed reviews from customers. Some customers have found errors and missing steps in the exercises which made it difficult to follow. The author also focusses a lot on NiFi, which some customers have found misleading. However, others have appreciated the conceptual understanding provided in the first half of the book. The book assumes a certain level of knowledge and may require additional research. Overall, the book could be useful for understanding DE principles, but readers may need to Consult the tools' documentations for more updated information.

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Avantages

  • Provides a conceptual understanding of DE principles.
  • Includes a variety of tools used in DE.
  • Could be useful for building a foundation on DE.

Les inconvénients

  • Contains errors and missing steps in exercises.

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Caractéristiques et avantages

  • 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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