• Title: 97 Things Every Data Engineer Should Know: Collective Wisdom from the Experts
• Author(s) Tobias Macey
• Publisher: O'Reilly Media; 1st edition (July 6, 2021); eBook (Compliments of Sigma)
• Permission: Free eBook Complimented by Sigma
• Hardcover/Paperback: 264 pages
• eBook: PDF and ePub
• Language: English
• ISBN-10/ASIN: 1492062413
• ISBN-13: 978-1492062417
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Book Description

Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small.

Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.

Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data.

Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers.

• Tobias Macey hosts the Data Engineering Podcast and Podcast.__init__ where he discusses the tools, topics, and people that comprise the data engineering and Python communities respectively.

• Amazon

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

• 97 Things Every Data Engineer Should Know: Collective Wisdom from the Experts
• Modern Data Engineering Playbook (ThoughtWorks)

• Fundamentals of Data Engineering Concepts (Sumit Chopra, et al.)

  The book is structured to provide a foundational understanding of data engineering, beginning with an overview that sets the stage for more detailed explorations, crafted to introduce readers to the core principles and practices of this crucial field.

• 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.

• Open Source Data Pipelines for Intelligent Applications

  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.

• 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.

• Fundamentals of Data Engineering: Build Robust Data Systems

  With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.

• 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.

• Big Data Engineering: Interview Questions and Answers

  Big data is all about vast amounts of data, i.e., large datasets measured in terabytes or petabytes or even more. Big data helps numerous companies to generate valuable insights about the products or the services they offer.

• 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.

• 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.