In this short report, five data industry thought leaders explore DataOps—the automated, process-oriented methodology for making clean, reliable data available to teams throughout your company.

Data Lakes presents recent outcomes and trends in the field of data repositories. The main topics discussed are the data-driven architecture of a data lake; the management of metadata – supplying key information about the stored data, master data and reference data; the roles of linked data and fog computing in a data lake ecosystem; and how gravity principles apply in the context of data lakes. A variety of case studies are also presented, thus providing the reader with practical examples of data lake management.

With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization.

This practical book provides a guided tour of these architectures to help data professionals understand the pros and cons of each. James Serra, big data and data warehousing solution architect at Microsoft, examines common data architecture concepts, including how data warehouses have had to evolve to work with data lake features. You'll learn what data lakehouses can help you achieve, as well as how to distinguish data mesh hype from reality. Best of all, you'll be able to determine the most appropriate data architecture for your needs.

Author Ole Olesen-Bagneux explains the benefits of implementing a data catalog. You'll learn how to organize data for your catalog, search for what you need, and manage data within the catalog.

Ready, Set, Curate shows you how to elevate the most important content from an endless sea of learning information and offers strategies to better connect with your audience. Using case studies and relevant examples, eight curation experts share tips and best practices for creating a curation strategy and collecting content that is relevant to your learning communities.

Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing.

This comprehensive guide walks you through tools and methodologies like Python, R, and command-line utilities to prepare raw data for analysis. Learn practical strategies to manage, clean, and refine data encountered in the real world.

This book guides you through the process of building modern, scalable ETL pipelines using Python. You'll explore practical techniques and best practices for every step-extracting data from various sources, transforming it effectively, and loading it into your desired destination.

This updated technical guide offers data engineers, engineering managers, and architects an overview of the modern ETL process, along with the challenges you're likely to face and the strategic patterns that will help you overcome them. You'll come away equipped to make informed decisions when implementing ETL and confident about choosing the technology stack that will help you succeed.

n this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve.