Big data is now the real deal for modern businesses, and the race to harness data's potential for profits is prevalent across most industries. With the global big data and analytics market surpassing US$348 million, businesses are seeking new ways to gather valuable insights without encountering roadblocks. Therefore, it’s crucial to take a step back and learn the basics of modern data architectures and how such frameworks can turn raw data into actionable insights.

In this article, we will discuss the basics of data architectures, along with their key principles and workings. We will also shed light on different architectural frameworks and emerging trends that are reshaping the modern data environment. 

But first, let’s understand what data architecture is for an organization. 

What is Data Architecture?

Data architecture provides a blueprint for using data. It acts as a framework for managing an organization's data-related resources. With a robust data architecture, data-driven organizations can monitor the acquisition, movement, storage, security, and overall accessibility of data across their IT infrastructures. 

Data architecture often serves as a bridge between raw data and business needs. Experts believe that the global big data market will rise to US$103 billion by 2027. With the rise of new data sources arising from innovative technologies such as the Internet of Things (IoT), data architecture offers efficient management for big data, streamlining the overall data lifecycle. 

Let’s take a closer look at the workings of a reliable data architecture.

How does Data Architecture Work?

Data architecture operates by developing a standardized data environment for harmonized operations. A multifaceted approach establishes an effective functioning of a data architecture. 

The following approaches define the workings of data architecture.

• Robust storage initiatives: Innovative storage solutions for data enable it to be accessible across multiple platforms. 
• Using appropriate data models: Data models determine the overall relationship among datasets and their structure. 
• Appropriate security: Security protocols ensure data integrity from potential attacks and incidents of data corruption. 
• Data governance: Robust data governance practices ensure your data remains compliant with specific laws and regulations. 

Together, these approaches ensure the proper functioning of a data architecture, offering organizations informed decision-making and strategic advantage for long-term success. 

Next, let’s understand the primary components that define a reliable data architecture. 

The Main Components of a Data Architecture

Data architecture is vast and complex and has defined structure-bearing components. These elements work cohesively to provide specific functionality and purpose to data architectures. 

The key components are as follows:

1. Data Models

Data models are the blueprints for designing data structures and categorizing data storage, access, and updates. They can be physical, logical, or conceptual and provide a layout for data flows and dependencies across various data systems.

2. Data Lakes and Warehouses

These are data storage solutions that support both current and future data needs for better scalability, reliability, and security. A robust data storage enhances overall data retrieval and management, providing better accessibility to data across multiple platforms. 

3. Data Governance

Data governance includes standards and policies for efficient data management across different architectures. These policies ensure data integrity is compliant with specific rules and regulations. A robust data governance framework establishes roles, accountability, and ethical data use across data workflows. 

4. Data Security 

These include precautionary measures to safeguard data from breaches and unauthorized access. Some of the common data security measures for a data-driven organization include access controls, encryption, and regular audits. Data security is a major component of ensuring data remains secure and compliant with changing regulatory standards and secure against potential threats. 

As we move forward with the basics of data architecture, let’s examine some of the different types of data architecture. 

5 Types of Data Architecture

As data architecture evolves, data continues to hold a major position in defining enterprise strategies. 

Here are some of the common types of data architectures:

1. Monolithic Architecture

• Enables users to view their data within a single tightly-packed unit. 
• It is an intertwined system that cohesively runs data within a single operating environment. 
• It simplifies the overall development and deployment operation in a streamlined fashion. 

2. Cloud-based Architecture

• The data architecture depends on off-site resources and reduces the dependence on diverse on-premise architectures. 
• Businesses benefit from improved flexibility, scalability, and streamlined costs. 

3. Hybrid Architecture

• This data architecture serves as a bridge between on-premise solutions and cloud architectures. 
• It bears the control of on-site systems and the scalability of cloud frameworks under one roof. 
• Businesses benefit from safeguarding their data in-house by reducing the computational tasks on the cloud. 

4. Kappa and Lambda Architecture

• These are specific to big data and offer swift data processing capabilities. 
• Lambda architectures deploy both batch and stream processing methods to ensure data accuracy. Kappa architectures, on the other hand, run using steam-processing methods.
• Despite being tailored for big data, the choice between the two architectures depends on specific requirements and processing preferences. 

5. Service-oriented architectures (SOA)

• SOA develops applications that communicate through HTTP. However, these architectures are not confined to a specific programming language. 
• The core strength of SOA architectures depends on reusability across different applications, providing consistency across multiple platforms. 

Now that we have a comprehensive understanding of different types of data architectures. Let’s see how you can build a robust data architecture with a few simple steps. 

Stepwise Guide to Building a Data Architecture

Here’s a step-by-step approach to creating a data architecture:

Step 1: Having a Data Strategy in Place

An ideal data strategy showcases the intent to use data to influence business decisions. Creating a data strategy highlights the areas that can improve business decisions and develop a robust data architecture. 

Step 2: Implementing Data Governance

The next step is to ensure appropriate data governance policies are in action for the architecture. Moreover, data governance also allows data to move beyond technical infrastructures. 

Step 3: Connecting Data architecture to Data Modelling

Data modeling enables organizations to understand their data structures within different databases clearly. At this stage, data models also ensure that architects use various components to enhance their business efficiency. 

A strong data architecture is the foundation of every data-driven action.

Now, let’s explore the key principles that define a data architecture framework. 

5 Principles of Data Architecture Framework

The principles of data architectures determine the guidelines for effective data management across an organization. The following principles guide a robust data environment, supporting both present and future business demands. 

1. Scalability

A scalable data architecture ensures enough space for rising data volumes. It also ensures that a data environment is ready to tackle a sudden influx of data for flexible analytics and reporting. 

2. Flexibility

A robust data architecture must be adaptive and capable of meeting changing business requirements. In addition, a flexible data architecture enables organizations to deploy novel innovations, such as artificial intelligence (AI) tools, with minimal disruptions. 

3. Availability

Data architecture needs to ensure data accessibility across different platforms. It enables businesses to make timely and informed decisions. 

4. Consistency

Uniformity is a key factor in a reliable data architecture. It involves using standardized formats for effortless data integration and analytics. 

5. Security

Data architecture requires security to protect data against potential threats and corruption. Maintaining security standards across data architectures also ensures data remains compliant with specific compliance standards. 

Organizational requirements and data needs should drive the design of a data architecture. The underlying data architecture frameworks are proving to be critical to supporting data-driven business strategy.  

3 Common Data Architecture Frameworks 

Several frameworks guide the implementation of data infrastructures within modern businesses.  Below are the most common types for a data-driven environment. 

1. TOGAF (The Open Group Architecture Framework)

• A high-level method for designing, planning, governing, and implementing data architectures. 
• TOGAF's primary focus is to align IT infrastructures with existing organizational objectives to develop a scalable data environment. 
• It also provides a strategic guide for creating structured data management for businesses. 

2. DAMA-DMBOK (Data Management Body of Knowledge)

• It offers comprehensive guidelines for determining the best practices for data management. 
• It covers key aspects of a robust data architecture, such as data governance, quality, and integration. 
• The framework highlights data management principles for achieving high-quality data across organizations. 

3. The Zachman Framework

• It efficiently organizes enterprise data by providing a basic overview of the data environment in a structured manner. 
• The framework ensures that there is effective communication between stakeholders with a common language to meet business objectives. 

Modern data architecture also serves as a platform for providing various mechanisms to effortlessly integrate data across multiple domains. Let’s explore the benefits of a scalable data architecture. 

Benefits of Data Architecture

With the proper implementation of a data architecture, organizations can expect the following benefits in the long run. 

1. Improved Data Lifecycle Management

A flexible and agile architecture ensures effective business management. Since data quickly degenerates, such features within an architecture allow for smooth data migration and improved accessibility across multiple platforms. 

2. Better Data Quality 

Data architecture benefits businesses with robust security standards and data governance policies, ensuring data remains of the highest quality. Organizations leveraging high-quality data architectures can access data with accuracy and make actionable decisions with ease. 

3. Reduced Data Silos

Data storage solutions that are disparate can often generate data silos. A scalable data architecture ensures flexible data integration across multiple domains, enabling cost-efficiencies and better decision-making. 

4. Minimal Redundancy

A well-organized data architecture reduces the chances of duplicate data generation, which can jeopardize the overall data environment. Moreover, it enhances overall data analytics and reporting to meet business objectives. 

Without the use of a robust data architecture, even the best data is just noise.

Now, let’s explore some recent trends that are redefining data architectures. 

Emerging Trends in Data Architecture

With the rise of big data, cloud technologies, and advanced solutions for data storage, modern organizations are looking for innovative technologies to meet their growing business demands. 

Here are some of the recent trends that are shaping the future of data architecture frameworks:

1. AI and ML analytics

AI and machine learning (ML) approaches are revamping the data landscape with robust predictive analytics, automation, and advanced data processing. Experts believe that the global market for AI will rise to over US$800 million by 2030. These cutting-edge technologies are reshaping and enhancing data architectures by making them more intelligent and adaptive for better decision-making processes. 

2. Blockchain

Blockchain creates a decentralized ledger for systems that boost data security and integrity in data environments.  It provides a transparent place for all data transactions and maintains the reliability of a data framework. 

3. Edge Computing

Edge computing reduces the distance between users and data centers. Edge computing is rising significantly and will have a revenue of US$110.6 billion by 2029. Effective integration of edge computing within a data architecture reduces overall latency and improves the real-time efficiency of data processing capabilities.

Modern data architectures are now the lifeline for any data-driven organization. Let’s see how QuartileX helps you build a reliable data architecture. 

QuartileX’s Data Engineering for Robust Data Architectures

At QuartileX, we ensure data remains scalable and businesses effortlessly gain valuable insights from their raw data. 

Here are some core data engineering features for a strong data architecture:

• Development of scalable data pipeline by optimizing ETL/ELT processes for data extraction, transformation, and management. 
• Creating data architectures that support large-scale data processing and smooth integration of AI and machine-learning pipelines. 
• Streamlined data access and integration that ensures data consistency and accuracy across multiple platforms. 

A well-structured data architecture is now essential to manage growing data demands. Did you know QuartileX leverages industry-leading tools like Hevo and debt to streamline data workflows? Take a closer look at our data engineering services to build a data architect that meets your business objectives.

Final Thoughts

Data architecture is the backbone of modern businesses, providing governance and accessibility for effortless data management. With the onset of emerging technologies such as edge computing and AI, organizations must create data architectures with appropriate flexibility to extract valuable insights for long-term success. 

With end-to-end expertise in data engineering at QuartileX, organizations can build data architectures that are adaptable to rising data demands. With the help of industry-leading tools and advanced automation, businesses can build data architectures that future-proof their operations for long-term success. 

Want to take your data architecture to the next level? Contact our data experts to explore our services that can boost your business ahead of the competition.