The Lakehouse Effect

Mark Simmonds, Steven Astorino, Roger Sanders

ISBN 10: 1583479074 / ISBN 13: 9781583479070

Editore: MC Press, LLC, US, 2024

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Data warehouses and data lakes each evolved to meet a set of specific technology and business needs and values. As organizations often need both, there has been increasing demand for convergence of both technologies. Thus, the lakehouse was born. A lakehouse couples the cost benefits and versatility of data lakes with the data structure and high-performance data management capabilities of data warehouses into a single unified data store that can be consistently and efficiently accessed, governed, analyzed and consumed by AI applications. Lakehouses are designed to help organizations get more from their existing investment in data warehouses and data lakes. It supports the existence of both through access to and management of a larger variety of combined data for increased flexibility, enhancing business intelligence and AI initiatives by revealing deeper insights into an organization's data estates. This book is intended for technical communities, such as developers, data scientists, and C-level IT executives, as well as business communities, such as business managers requiring self-service analytics / AI, and C-level business executives.

Codice articolo LU-9781583479070

Dati bibliografici

• Titolo: The Lakehouse Effect
• Editore: MC Press, LLC, US
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 1583479074
• ISBN 13: 9781583479070
• Rilegatura: Paperback
• Disegnatore: Sanders, Roger
• Condizione: New

Descrizione articolo

Riassunto

Data warehouses and data lakes each evolved to meet a set of specific technology and business needs and values. As organizations often need both, there has been increasing demand for convergence of both technologies. Thus, the lakehouse was born. A lakehouse couples the cost benefits and versatility of data lakes with the data structure and high-performance data management capabilities of data warehouses into a single unified data store that can be consistently and efficiently accessed, governed, analyzed and consumed by AI applications. Lakehouses are designed to help organizations get more from their existing investment in data warehouses and data lakes. It supports the existence of both through access to and management of a larger variety of combined data for increased flexibility, enhancing business intelligence and AI initiatives by revealing deeper insights into an organization’ s data estates. This book is intended for technical communities, such as developers, data scientists, and C-level IT executives, as well as business communities, such as business managers requiring self-service analytics / AI, and C-level business executives.

Informazioni sull?autore

Mark Simmonds is a Program Director in IBM Data and AI communications. He writes extensively on machine learning and data science, holding a number of author recognition awards. He previously worked as an IT architect, leading complex infrastructure design projects. He is a member of the British Computer Society and holds a Bachelor's Degree in Computer Science. Roger E. Sanders is a Principal Sales Enablement & Skills Content Specialist at IBM. He has worked with Db2 (formerly DB2 for Linux, UNIX, and Windows) since it was first introduced on the IBM PC (1991) and is the author of 26 books on relational database technology (25 on Db2; one on ODBC). For 10 years he authored the “ Distributed DBA” column in IBM Data Magazine, and he has written articles for publications like Certification Magazine, Database Trends and Applications, and IDUG Solutions Journal (the official magazine of the International Db2 User's Group). Steven Astorino is the Vice President of Development, Data and AI and Canada Lab Director at IBM. He is a development executive with proven transformational leadership and expertise in leading large enterprise development. He has been leading and driving the machine learning and data science strategy for IBM's analytics group. Steve has a Bachelor's Degree in Computer Science.