Salesforce Data Architecture and Management: A pragmatic guide for aspiring Salesforce architects and developers to manage, govern, and secure their data effectively

Ahsan Zafar

ISBN 10: 1801073244 / ISBN 13: 9781801073240

Edité par Packt Publishing Limited, 2021

A propos de cet article

This item is printed on demand. New copy - Usually dispatched within 5-9 working days 100.

N° de réf. du vendeur C9781801073240

Détails bibliographiques

• Titre: Salesforce Data Architecture and Management: A pragmatic guide for aspiring Salesforce architects and developers to manage, govern, and secure their data effectively
• Éditeur: Packt Publishing Limited
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 1801073244
• ISBN à 13 chiffres: 9781801073240
• Reliure: Paperback / softback
• État de l'article: New

A propos de ce titre

Synopsis

Learn everything you need to become a successful data architect on the Salesforce platform

Key Features:

• Adopt best practices relating to data governance and learn how to implement them
• Learn how to work with data in Salesforce while maintaining scalability and security of an instance
• Gain insights into managing large data volumes in Salesforce

Book Description:

As Salesforce orgs mature over time, data management and integrations are becoming more challenging than ever. Salesforce Data Architecture and Management follows a hands-on approach to managing data and tracking the performance of your Salesforce org.

You'll start by understanding the role and skills required to become a successful data architect. The book focuses on data modeling concepts, how to apply them in Salesforce, and how they relate to objects and fields in Salesforce. You'll learn the intricacies of managing data in Salesforce, starting from understanding why Salesforce has chosen to optimize for read rather than write operations. After developing a solid foundation, you'll explore examples and best practices for managing your data. You'll understand how to manage your master data and discover what the Golden Record is and why it is important for organizations. Next, you'll learn how to align your MDM and CRM strategy with a discussion on Salesforce's Customer 360 and its key components. You'll also cover data governance, its multiple facets, and how GDPR compliance can be achieved with Salesforce. Finally, you'll discover Large Data Volumes (LDVs) and best practices for migrating data using APIs.

By the end of this book, you'll be well-versed with data management, data backup, storage, and archiving in Salesforce.

What You Will Learn:

• Understand the Salesforce data architecture
• Explore various data backup and archival strategies
• Understand how the Salesforce platform is designed and how it is different from other relational databases
• Uncover tools that can help in data management that minimize data trust issues in your Salesforce org
• Focus on the Salesforce Customer 360 platform, its key components, and how it can help organizations in connecting with customers
• Discover how Salesforce can be used for GDPR compliance
• Measure and monitor the performance of your Salesforce org

Who this book is for:

This book is for aspiring architects, Salesforce admins, and developers. You will also find the book useful if you're preparing for the Salesforce Data Architecture and Management exam. A basic understanding of Salesforce is assumed.

À propos de l?auteur

Ahsan Zafar is Salesforce Technical Architect. He is 15x Salesforce certified and is a Salesforce Certified Technical Architect (CTA) candidate. He has spent around two decades working with different aspects of data, including data architecture, master data management, and data migrations. Ahsan has also had stints as a Business Analyst, Project Manager, and Salesforce consultant on various projects that gave him a unique understanding of how data is perceived by different roles. He has also worked as a developer and as a technical lead, spending almost a decade implementing Oracle ERP systems that typically involved migrating significant volumes of data from legacy systems.