Chapter 2: Core Architectural Concepts – Data

As you learned in the previous chapter, the expectations are really high for a Salesforce CTA. The architect is expected to have a deep understanding of the platform's features and capabilities. In addition to a very deep understanding of particular architectural concepts, the architect is also expected to have a breadth of knowledge across many architectural domains. Data is one of the key architectural domains that architects need to master. Data is all around us in our daily lives and every day, we discover a new potential for it. From sales to service, marketing, business intelligence, and artificial intelligence, data is at the heart of today's modern systems.

In this chapter, we're going to cover the following main topics:

• Differences between classic RDBMS and Salesforce
• Understanding data governance
• Understanding data security
• Data regulatory compliance
• Exploring data categories
...

Differences between classic RDBMS and Salesforce

People have used databases in their day-to-day activities for centuries. Although they have only been given the name "databases" recently, they have been developed for years and we've invented more and more use cases for them. Most modern applications utilize a database of some sort. Theoretically, a database is simply a collection of related data. We call the software system that manages this data a database management system (DBMS). The DBMS will also be responsible for controlling access to the database.

Databases have evolved over the years from simple file-based systems to sophisticated cloud-based relational database management systems and in-memory databases.

Understanding the problems of file-based systems could help you avoid challenges that could occur in modern database systems. File-based systems were designed for a specific set of use cases. This was primarily driven by an attempt to digitalize the activities...

Understanding data governance

Data governance, in an enterprise context, is a data management concept that aims to ensure a high level of data quality throughout the complete life cycle of the data.

The data governance concept can be extended to several focus areas. Enterprises typically focus on topics such as data usability, availability, security, and integrity. This includes any required processes that need to be followed during the different stages of the data life cycle, such as data stewardship, which ensures that the quality of the data is always up to a high standard, and other activities that ensure the data is accessible and available for all consuming applications and entities.

Data governance aims to do the following:

• Increase consistency and confidence in data-driven decisions. This, in turn, enables better decision-making capabilities across the enterprise.
• Break down data silos.
• Ensure that the right data is used for the right purposes. This is...

Understanding data security

Data security is one of the greatest concerns for enterprises today, especially with the ever-increasing amount and value of collected data. It is all about protecting digital data from the actions of unauthorized users (such as data leaks or breaches) or from destructive forces. As part of that, there are a few concepts that you need to become familiar with, including encryption, backup and restore, data masking, and data erasure. Let's get to know each of them.

Data encryption

Data encryption can be applied at multiple levels and stages of the data life cycle. This includes when the data is stored at its final data store (encryption at rest) and while data is in motion, moving from one system to the other (encryption in transit).

Encryption in transit is typically achieved by encrypting the message before it is transmitted and decrypted at the destination. This process intends to protect data while being transferred against attackers who...

Data regulatory compliance

There are several data regulations in place based on industry standards and government regulations. These have been created to mitigate the risk of unauthorized access to corporate, personal, or government data. Some of the regulations that you may come across more frequently these days while designing a Salesforce solution include the following:

• General Data Protection Regulation (GDPR)
• United Kingdom version of GDPR (UK-GDPR)
• Health Insurance Portability and Accountability Act (HIPAA)
• California Consumer Privacy Act (CCPA)
• Fair and Accurate Credit Transactions Act of 2003 (FACTA)
• Act on the Protection of Personal Information (APPI)
• Gramm-Leach-Bliley (GLB)
• Payment Card Industry Data Security Standard (PCI DSS)

Failure to comply with these regulations can result in hefty fines and public damage to the enterprise's reputation, in addition to civil and criminal liability. We are not going to cover the details...

Exploring data categories

Reference data and master data are two common data categories that an architect would typically come across in most projects. In addition to these, in this section, we will also discover the characteristics of transactional data, reporting data, metadata, big data, and unstructured data. Having a deeper understanding of these different data categories will help you craft your overall data strategy, including data governance. This will also help you speak the same language your data architects prefer to use. Let's have a look at each of them closely.

Transactional data

Transactional data is generated by regular business transactions. It describes business events. Normally, it is the most frequently changing data in the enterprise. Transactional data events could include the following:

• Sold products to customers
• Collected payments
• Created quotes
• Shipped items to customers

Transactional data is normally generated and managed...

The nature of data warehouses and data lakes

A data warehouse (DW or DWH) is a central repository of current and historical data that's been integrated from one or more disparate sources. The DWH (also referred to as enterprise data warehouse (EDW)) is a system that's used for data analysis and reporting. It is usually considered the core of an enterprise business intelligence strategy.

Data stored in a DWH comes from multiple systems, including operational systems (such as CRM systems). The data may need to undergo a set of data cleansing activities before it can be uploaded into the DWH to ensure data quality.

Some DWH tools have built-in ETL capabilities, while others rely on external third-party tools (we will cover ETL tools and other integration middleware in Chapter 3, Core Architectural Concepts – Integration and Security). This ETL capability will ensure that the ingested data has a specific quality and structure. Data might be staged in a specific staging...

Choosing the right document management system

Electronic documents are everywhere nowadays in enterprises. An electronic document can simply be thought of as a form of electronic media content that can be used either in its electronic form or as printed output. This does not include system files or computer applications. A document management system (DMS) is a computer application that's used to store, track, and manage electronic documents throughout their life cycles. This includes activities related to these documents, such as versioning and workflows.

Document management overlaps with content management, even though they were originally different in nature. They are often seen combined today as enterprise content management (ECM) systems, mainly because many of today's electronic documents that are available for a particular enterprise are not necessarily generated by the enterprise itself.

DMSes in today's age differ in terms of complexity, features, and scope...

Understanding data architecture concepts

Previously, we covered the importance of data, as well as the importance of data architecture for the enterprise. The data architect – who you, as a Salesforce architect need to work closely with or, in smaller projects, act as one – needs to tackle the data architecture in a similar fashion to a normal building architecture. And to a good extent, similar principles should be followed in software architecture.

First, it is imperative to understand the business processes and create a conceptual and logical blueprint. Then, you need to know the underlying technology in order to build the detailed design and implementation. To understand these better, let's take a closer look at the three stages of data architecture design.

Conceptual-level data architecture design

The data architect, at the conceptual-level stage, needs to gain a deep understanding of the business knowledge, business processes, and operations. This...

Designing and documenting your data model

The importance of your data model's design cannot be emphasized enough. It is sad to see it being overlooked in many cases in Salesforce due to the fact that Salesforce comes with a pre-built data model, in addition to great flexibility for creating and changing custom fields and objects.

However, the proper data model design could be the difference between a smart, flexible, and scalable solution that delivers valuable real-time insight that justifies all efforts required to enter the data. This may also glorify the database with tons of dumped data and provide an overall feeling that data entry is an overhead.

Your data model is the foundation of your solution. You can read through the solution by understanding its data model, and your solution should be as strong as its foundation. You need to get your data model design right, and in order to do so, you need to understand key concepts in data modeling. This starts with normalization...

Summary

In this chapter, you learned some historical information about database design, as well as how a proper design can be crucial to the overall Salesforce solution. You also learned about some key concepts, such as reference data, reporting data, and big data, in addition to general information about document management systems.

We then covered various data modeling principles and activities, including the process of normalizing and denormalizing data. We also covered other common architectural knowledge areas, such as normal forms, database relationships, and cardinality.

All this knowledge will be required when you're designing your secure and scalable Salesforce solution. In the next chapter, we will cover another set of required common architectural skills. This time, we will focus on the ever-growing and always-interesting integration domain.