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

What is this book covers

Chapter 1, Data Architect-Roles and Responsibilities, describes the role of a data architect and the core skills and experience that are required for it. It will also go in to detail on what soft skills are required to be successful in the role. You will also get to have a look at a day in the life of a data architect.

Chapter 2, Understanding Salesforce Objects and Data Modeling, will take you through the unique architecture of the Salesforce platform and how it is optimized for read access rather than write operations traditionally seen in relational databases. Data modeling concepts, how they get applied in the context of Salesforce, what de-normalization is, and why it is important to spend the time designing your data model properly will be discussed. At the end of the chapter, Salesforce objects and how they are created, different types of fields on them, and their use cases will be covered.

Chapter 3, Understanding Data Management, will explain data management, what it is, and why it's important. The different aspects of managing data, including the data lifecycle, will also be discussed. With Salesforce's discontinuation of data recovery services, data backup and archiving have come to the forefront, so we will discuss that in detail as well. At the end, some tools that are available to manage data effectively will be reviewed.

Chapter 4, Making Sense of Master Data Management, will discuss the key attributes of master data, what the Golden Record is and why it is so important for organizations. We will look at how to align your MDM and CRM strategy with a discussion on Salesforce's Customer 360 and its key components. MDM is a platform-agnostic concept that can be used within the context of non-Salesforce landscapes as well. The chapter is concluded with a brief discussion of the Common Information Model (CIM).

Chapter 5, Implementing Data Governance, covers the importance of enterprise data governance, the relationship between data governance and data management, and how to assess the current state of data governance. Two major privacy protection laws, the GDPR and CCPA, will also be covered in detail. To conclude and firm up understanding of the content, a sample case study will describe a hypothetical scenario and the solution approach to solve it.

Chapter 6, Managing Performance, will explore foundational aspects of performance on the platform, how to use the Query Plan tool to determine performance-impacting queries, and query costs when using indexes versus full table scans. The chapter also covers the various tools that can be used to monitor the platform for performance and auditing changes. Multiple code blocks will be used to drive the point home of how performance can be determined and optimized. Performance testing is critical especially when dealing with large volumes of data, so an extensive discussion around aspects of performance testing will be covered, followed by a discussion on monitoring the performance of the Salesforce org.

Chapter 7, Working with Large Volumes of Data, will introduce you to the concept of relational and non-relational databases. LDVs, which are becoming more and more relevant in the Salesforce ecosystem as orgs generate or consume lots of data, will be discussed extensively, from identifying LDV scenarios to managing LDV orgs and integrating data into these org types.

We will look at some options in cases where large volumes of data don't necessarily have to be brought into Salesforce, but the data can still be made available to users. We will cap our discussion with Big Objects which is yet another way to deal with very large data volumes.

Chapter 8, Best Practices for General Data Migration, will introduce you to data migration - how to assess, plan, and execute data migrations. Considerations and best practices related to data migration will also be discussed. Close to the end, we will discuss some commonly used tools that can be used for data migration. We will cap our discussion by discussing the different APIs that are available in Salesforce within the context of data migration.