Gaining Data Agility with Multi-Model Databases

Chapter 1. Introduction

Most organizations realize that their future depends on their ability to quickly adapt to the constant changes brought on by an ever-changing and complex environment. Without this agility, an organization’s IT systems become stagnant and eventually ineffective. Because of this, enormous amounts of pressure are placed on the CIO and the organization’s technology centers to come up with innovative solutions to complex processes and problems. More and more we are seeing that the core source behind these innovative solutions is data. This is because within most organizations, there is no shortage of data.

Now that we have all of this data, we’re going to need to provide access to users so that they can start to create meaningful and relevant solutions. How we decide to enable this access will be critical. And although there are many different approaches and technologies that we can use to achieve this, each approach serves a specific purpose and use case but typically leaves gaps that need to be filled with additional technologies, tools, and integrations. For example, tabular data that we see in RDBMS-based systems require an up-front investment of time to model data. This up-front cost often results in complex data models that are to rigid to support changing data sources and requirements. Other types of data that can be helpful in meeting a system’s requirements might include document-based data (XML or JSON), semantic data, and textual data.

Having access ...