Modern Enterprise Data Architecture
Data plays a vital role in conceptualizing the preliminary design for an architecture. You may want to decide the requirements for security, performance, and infrastructure to handle workload, scalability, and agility in design. In this case, you need to understand data models and how to handle architectural decisions, including data privacy and security, compliance requirements, data size to handle, and user handling requirements.
This is the reason that data-driven architecture is the driving factor for an enterprise design development. The modern enterprise architectures that are referred to in this article include microservices, cloud-native applications, event-driven solutions, and data-intensive solutions. The article intends to share modern enterprise data architecture perspectives, including solution approaches and architectural models to develop new-age solutions catering to velocity, veracity, volume, and variety of data handling services.
A recent data architecture trend based on various case studies recommends the move toward polyglot persistence, which is a group of multiple types of data storage technologies for integrated architecture. Integrated architecture is needed to provide high performance for any type of data processing across different services. Typically used in cloud adoption, this kind of implementation is supported by Database as a Service (DBaaS). DBaaS is implemented for the following benefits:
This type of polyglot-persistence-based microservices architecture helps to develop resilient, robust, and high-performing architecture to support variant types of data services for different microservices (see Figure 2).
In the example above, each microservice handles data at different capacities and performance requirements. Hence, based on these data access requirements, the choice of database can be picked to cater to performance, scalability, and the optimal data model to be stored in the system. This leverages microservices to make cost-effective and agile architectures in a polyglot persistence model.
In traditional architecture development, data modeling is the simple task of deriving data elements from requirements, depicting the relation between the entities through entity relationship (ER) diagrams, and defining the parameters (data types, constraints, validations) around the data elements. This means that data modeling is done as a single-step activity in a traditional architecture by defining the data definition language (DDL) scripts from requirements.
In modern enterprise data architecture, this is split into a multi-stage activity as conceptual, logical, and physical data modeling, as illustrated below in Figure 3. In a data-driven architecture where data intensity is high, data modeling is a fundamental and crucial step (and, of course, time-consuming). In such an architecture development, data modeling is innovatively divided into three different types:
Data intelligence and data analytics are modern techniques used in modern enterprise data architectures for NoSQL databases to handle big data as well as data-intensive application architectures. It involves one or more of the popular technology solutions, like cloud platforms for agility and scalability of solutions, AI/ML for advanced algorithms to build intelligence in data processing, and big data platforms to handle the storage and analysis of the data.
Data intelligence handles data for visualization and analytics by using predictive intelligence to focus the data so that it’s visualized for the future (forecasting). How a stock for an enterprise has trended so far is an example of data intelligence, and data analytics is using history to predict how it will change in the next year.
