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Data teams : a unified management model for successful data-focused teams /
Learn how to run successful big data projects, how to resource your teams, and how the teams should work with each other to be cost effective. This book introduces the three teams necessary for successful projects, and what each team does. Most organizations fail with big data projects and the failu...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Apress,
[2020]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 020 | |a 148426228X |q electronic book | ||
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| 020 | |z 1484262271 | ||
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| 037 | |a com.springer.onix.9781484262283 |b Springer Nature | ||
| 037 | |a 56316A1F-A447-46CC-BAA7-A4EF5A10BAB3 |b OverDrive, Inc. |n http://www.overdrive.com | ||
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| 049 | |a UAMI | ||
| 100 | 1 | |a Anderson, Jesse, |e author. | |
| 245 | 1 | 0 | |a Data teams : |b a unified management model for successful data-focused teams / |c Jesse Anderson. |
| 264 | 1 | |a New York, NY : |b Apress, |c [2020] | |
| 300 | |a 1 online resource (xxiv, 294 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 505 | 0 | |a Part 1: Introducing data teams. Chapter 1: Data teams -- Chapter 2: The good, the bad, and the ugly data teams -- Part 2: Building your data team. Chapter 3: The data science team -- Chapter 4: The data engineering team -- Chapter 5: The operations team -- Chapter 6: Specialized staff -- Part 3: Working together and managing the data teams. Chapter 7: Working as a data team -- Chapter 8: How the business interacts with data teams -- Chapter 9: Managing big data projects -- Chapter 10: Starting a team -- Chapter 11: The steps for successful big data projects -- Chapter 12: Organizational changes -- Chapter 13: Diagnosing and fixing problems -- Part 4: Case studies and interviews. Chapter 14: Interview with Eric Colson and Brad Klingenberg -- Chapter 15: Interview with Dmitriy Ryaboy -- Chapter 16: Interview with Bas Geerdink -- Chapter 17: Interview with Harvinder Atwal -- Chapter 18: Interview with a large British telecommunications company -- Chapter 19: Interview with Mikio Braun. | |
| 520 | |a Learn how to run successful big data projects, how to resource your teams, and how the teams should work with each other to be cost effective. This book introduces the three teams necessary for successful projects, and what each team does. Most organizations fail with big data projects and the failure is almost always blamed on the technologies used. To be successful, organizations need to focus on both technology and management. Making use of data is a team sport. It takes different kinds of people with different skill sets all working together to get things done. In all but the smallest projects, people should be organized into multiple teams to reduce project failure and underperformance. This book focuses on management. A few years ago, there was little to nothing written or talked about on the management of big data projects or teams. Data Teams shows why management failures are at the root of so many project failures and how to proactively prevent such failures with your project. You will: Discover the three teams that you will need to be successful with big data Understand what a data scientist is and what a data science team does Understand what a data engineer is and what a data engineering team does Understand what an operations engineer is and what an operations team does Know how the teams and titles differ and why you need all three teams Recognize the role that the business plays in working with data teams and how the rest of the organization contributes to successful data projects. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on May 10, 2022). | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Database management. | |
| 650 | 0 | |a Electronic data processing departments |x Management. | |
| 650 | 0 | |a Data processing service centers |x Management. | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Bases de données |x Gestion. | |
| 650 | 6 | |a Centres de traitement de l'information |x Gestion. | |
| 650 | 7 | |a Databases. |2 bicssc | |
| 650 | 7 | |a Computers |x Database Management |x General. |2 bisacsh | |
| 650 | 7 | |a Data processing service centers |x Management |2 fast | |
| 650 | 7 | |a Electronic data processing departments |x Management |2 fast | |
| 650 | 7 | |a Database management |2 fast | |
| 650 | 7 | |a Big data |2 fast | |
| 776 | 0 | 8 | |i Print version: |z 1484262271 |z 9781484262276 |w (OCoLC)1176326913 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781484262283/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH37890062 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL6354339 | ||
| 938 | |a EBSCOhost |b EBSC |n 2630913 | ||
| 938 | |a YBP Library Services |b YANK |n 16965193 | ||
| 994 | |a 92 |b IZTAP | ||