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54659559
Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python
89% of respondents would recommend this to a friend
Item #:
54659559
NIO 3182
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This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python.
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What Stands Out
Massive Datasets
Learn how to manage and manipulate large datasets effectively, ensuring data integrity and optimized performance for real-world applications.
Data Models Design
Gain essential skills in designing robust data models that cater to various business needs, enhancing data accessibility and usability.
Automated Pipelines
Master the automation of data pipelines using Python, streamlining workflows and reducing manual intervention for increased efficiency and reliability.
Product Details
- 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 | 23 Oct. 2020 |
| Language | English |
| Print length | 356 pages |
| ISBN-10 | 183921418X |
| ISBN-13 | 978-1839214189 |
| Item weight | 612 g |
| Dimensions | 19.05 x 2.06 x 23.5 cm |
Who Should Buy?
-
Aspiring Data Engineers
Ideal for individuals seeking to enter the data engineering field with practical skills and knowledge using Python.
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Data Analysts
Helpful for analysts looking to enhance their skills in managing and manipulating large datasets effectively.
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Python Developers
Valuable for developers wanting to expand their expertise into data engineering and related technologies.
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Beginners in Programming
Not suitable for those without basic programming knowledge as it may be overwhelming and complex.
Product Description
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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Customer Questions & Answers
-
Question:
Who is this book for?
Answer: This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering, or refresh their knowledge of data engineering using Python. -
Question:
Is previous knowledge of data engineering required?
Answer: No, previous knowledge of data engineering is not required. -
Question:
What will I learn from this book?
Answer: Readers will learn data modeling techniques, how to build and deploy data pipelines, perform quality checks, and make necessary changes in production.
Data Warehousing Editorial Review
**** "Data Engineering with Python" serves as a practical guide for those looking to delve into the realms of data engineering, particularly using tools like NiFi and Airflow for orchestrating data pipelines. Readers have noted that the book excels in detailing NiFi, covering a range of topics from basic usage to advanced concepts such as deploying to production and versioning. This comprehensive approach makes it a valued resource, especially given the scarcity of NiFi tutorials in the market. While the content on Airflow provides a solid introduction, it falls short compared to dedicated works on the subject. However, it still offers enough insight for readers wanting to understand how to set up Directed Acyclic Graphs (DAGs) for basic data pipelines. The final sections introduce Kafka and Spark, although these are presented more as overviews rather than exhaustive guides, suggesting that additional resources will be necessary for a deeper understanding. One criticism of the book is its reliance on manual installation instructions for the tools discussed. Many readers expressed a preference for a Docker-based setup to streamline the installation process, highlighting the modern expectations for scalable solutions in data engineering. Additionally, the title of the book stretches beyond its actual breadth of content, leading to some confusion about its focus. Overall, despite these critiques, the positive reception of the book hinges on its effectiveness in explaining NiFi and its insights into data engineering, making it a recommended read for those specifically interested in that tool. **
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Pros
- Comprehensive coverage of NiFi, including advanced topics.
- Good introduction to data engineering concepts and tools.
- Valuable resource for learning NiFi due to the scarcity of available tutorials.
Cons
- Manual installation instructions may hinder some readers' progress.
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Features & Benefits
- 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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