Book Image

Salesforce Data Architecture and Management

By : Ahsan Zafar
Book Image

Salesforce Data Architecture and Management

By: Ahsan Zafar

Overview of this book

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.
Table of Contents (14 chapters)
Section 1: Data Architecture and Data Management Essentials
Section 2: Salesforce Data Governance and Master Data Management
Section 3: Large Data Volumes (LDVs) and Data Migrations

Introducing the data life cycle

In this section, we will look at the data life cycle and then look at an example within the context of Salesforce. The data life cycle provides a high-level overview of data from the time it is created to the time it is purged. Having clarity regarding how data gets created and moves throughout the life cycle helps in understanding where the activities that we perform daily fit, from an operational point of view, and how they are important. There are five stages a specific element of data will go through in its lifetime:

  1. Data creation
  2. Storage
  3. Usage
  4. Archival
  5. Purge

We will now look at each of these in more detail.

Data creation

This is the first phase of the data life cycle. In this phase, data is created or acquired, which can happen in many ways. Consider the example of Dun & Bradstreet, which provides data enrichment capabilities for Salesforce. A more common example is the use of an IT application to create...