Apply this definitive guide to data catalogs and select the feature set needed to empower your data citizens in their quest for faster time to insight.

The data catalog may be the most important breakthrough in data management in the last decade, ranking alongside the advent of the data warehouse. The latter enabled business consumers to conduct their own analyses to obtain insights themselves. The data catalog is the next wave of this, empowering business users even further to drastically reduce time to insight, despite the rising tide of data flooding the enterprise.

Use this book as a guide to provide a broad overview of the most popular Machine Learning (ML) data catalog products, and perform due diligence using the extensive features list. Consider graphical user interface (GUI) design issues such as layout and navigation, as well as scalability in terms of how the catalog will handle your current and anticipated data and metadata needs.

This book is organized into three sections:

• Chapters 1 and 2 reveal the rationale for a data catalog and share how data scientists, data administrators, and curators fare with and without a data catalog.
• Chapters 3-10 present the many different types of data catalogs.
• Chapters 11 and 12 provide an extensive features’ list, current trends, and visions for the future.

Apply this definitive guide to data catalogs and select the feature set needed to empower your data citizens in their quest for faster time to insight.

The data catalog may be the most important breakthrough in data management in the last decade, ranking alongside the advent of the data warehouse. The latter enabled business consumers to conduct their own analyses to obtain insights themselves. The data catalog is the next wave of this, empowering business users even further to drastically reduce time to insight, despite the rising tide of data flooding the enterprise.

Use this book as a guide to provide a broad overview of the most popular Machine Learning (ML) data catalog products, and perform due diligence using the extensive features list. Consider graphical user interface (GUI) design issues such as layout and navigation, as well as scalability in terms of how the catalog will handle your current and anticipated data and metadata needs.

This book is organized into three sections:

• Chapters 1 and 2 reveal the rationale for a data catalog and share how data scientists, data administrators, and curators fare with and without a data catalog.
• Chapters 3-10 present the many different types of data catalogs.
• Chapters 11 and 12 provide an extensive features’ list, current trends, and visions for the future.

Apply this definitive guide to data catalogs and select the feature set needed to empower your data citizens in their quest for faster time to insight.

The data catalog may be the most important breakthrough in data management in the last decade, ranking alongside the advent of the data warehouse. The latter enabled business consumers to conduct their own analyses to obtain insights themselves. The data catalog is the next wave of this, empowering business users even further to drastically reduce time to insight, despite the rising tide of data flooding the enterprise.

Use this book as a guide to provide a broad overview of the most popular Machine Learning (ML) data catalog products, and perform due diligence using the extensive features list. Consider graphical user interface (GUI) design issues such as layout and navigation, as well as scalability in terms of how the catalog will handle your current and anticipated data and metadata needs.

This book is organized into three sections:

• Chapters 1 and 2 reveal the rationale for a data catalog and share how data scientists, data administrators, and curators fare with and without a data catalog.
• Chapters 3-10 present the many different types of data catalogs.
• Chapters 11 and 12 provide an extensive features’ list, current trends, and visions for the future.