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Book synopsis

Mastering Python for Data Engineering: Transform and Manipulate Big Data with Python

Unlock the true potential of Python for big data manipulation and engineering with Mastering Python for Data Engineering. This comprehensive guide is designed to help data engineers and aspiring professionals transform, process, and analyze massive datasets efficiently. By leveraging Python's powerful libraries and tools, you'll be equipped to build scalable data pipelines, integrate various data sources, and optimize data workflows for performance.

From basic data wrangling to advanced engineering techniques, this book provides a practical, hands-on approach to mastering data engineering tasks with Python, making it the perfect companion for anyone aiming to work with big data.

What You'll Learn:

• The fundamentals of Python for data engineering, including essential libraries like pandas, NumPy, and Dask.
• Building efficient data pipelines for ETL (Extract, Transform, Load) processes.
• Working with large datasets using parallel and distributed processing tools like Apache Spark and Dask.
• Integrating data from various sources, such as databases, APIs, and streaming data.
• Data transformation and cleaning techniques to prepare data for analysis.
• Optimizing performance and scaling data workflows with Python.

With step-by-step guidance and practical examples, Mastering Python for Data Engineering will show you how to handle data at scale, integrate different data sources, and build automated data workflows that are crucial for modern data infrastructure.

Dive into the world of data engineering with Python and learn how to transform raw data into actionable insights while building systems that can handle vast amounts of information.

Book details

Book category Books in German Naturwissenschaften, Medizin, Informatik, Technik Informatik, EDV Informatik

• Full title: Mastering Python for Data Engineering
• Author: THOMPSON CARTER
• Language: English
• Binding: Paperback
• Number of pages: 270
• EAN: 9798305917345
• ID: 47227754
• Publisher: Amazon Digital Services LLC - Kdp
• Weight: 363 g
• Dimensions: 229 × 152 × 14 mm
• Date of publishing: 09. January 2025

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