• Title: Open Source Data Pipelines for Intelligent Applications
• Author(s) Kyle Bader, Sherard Griffin, Pete Brey, Daniel Riek, Nathan LeClaire
• Publisher: O'Reilly Media (2022); eBook (Compliments of Red Hat)
• Permission: Free eBook is Complimented by Red Hat
• Hardcover/Paperback: N/A
• eBook: PDF
• Language: English
• ISBN-10/ASIN: N/A
• ISBN-13: N/A
• Share This:  

Book Description

This book provides data engineers and scientists insight into how Kubernetes provides a platform for building data platforms that increase an organization’s data agility. How Kubernetes has changed the way we process big data and why businesses must adapt.

• N/A

• Amazon

• Data Engineering
• Data Analysis and Data Mining
• Big Data
• Machine Learning
• Unix/Linux Shell Scripting

• Open Source Data Pipelines for Intelligent Applications (Kyle Bader, et al.)
• The Mirror Site (1) - PDF

• 97 Things Every Data Engineer Should Know (Tobias Macey)

  With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Experts share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.

• Data Engineering Teams: Creating Big Data Teams and Products

  Unlock the secrets of Big Data and AI projects. We've always had teams for managing databases, but with the data management landscape so rapidly evolving – you need to take your data teams to a whole new level.

• The Evolving Role of the Data Engineer (Andy Oram)

  If you're pursuing a career in data engineering or looking for ways to adapt your enterprise to the world of big data, this report shares the knowledge you need to find your way forward.

• Data Science at the Command Line, 2nd Ed. (Jeroen Janssens)

  This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. Learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.

• Data Engineering Cookbook: The Plumbing of Data Science

  This is a practical and comprehensive guide. You will learn the basics of data engineering. Then you will learn the technologies and frameworks required to build data pipelines to work with large datasets.

• The Ultimate Guide to Effective Data Cleaning

  With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Experts share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.

• The Data Engineer's Guide to Apache Spark (Databricks)

  This book is for data engineers looking to leverage the immense growth of Apache Spark to build faster and more reliable data pipelines. It leverages Spark's amazing speed, scalability, simplicity, and versatility to build practical Big Data solutions.

• Building the Data Lakehouse (Bill Inmon, et al.)

  Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. The data lakehouse is the next generation of the data warehouse and data lake.

• Python Data Science Handbook: Essential Tools (Jake VanderPlas)

  Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all - IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

• Data Science: Theories, Models, Algorithms, and Analytics

  It provides a bucket full of information regarding Data Science, covers a wide variety of sections by giving access to theories, data science algorithms, tools and analytics. You'll explore the right approach to best practices to guide you along the way.

• 97 Things Every Cloud Engineer Should Know: from the Experts

  With this book, professionals from around the world provide valuable insight into today's cloud engineering role. It explore the entire cloud computing experience, including fundamentals, architecture, and migration.