What is Modern Data Architecture and How Does it Help Modern Businesses Compete in the Industry? 

Every business today is faced with the challenge of managing big data for strategic insights and informed decision-making. Enterprises need a robust data management system to leverage the growing volume, velocity, and variety of data. Legacy data systems pose numerous data challenges, making way for modern data architecture. 

What is Modern Data Architecture

Modern data architecture is a contemporary approach to building and deploying data systems that can handle the challenges presented by big data more effectively and unlock new opportunities in business. A modern data architecture encompasses a set of practices, technologies, and methodologies to support the advanced analytics and data processing needs of modern enterprises. Enterprises adopting a modern data architecture overcome the limitations of traditional data systems such as data silos and scalability constraints and leverage the advanced capabilities of cloud technologies for a competitive edge. 

In this blog post, we’ll take a closer look at some of the key characteristics and components of modern data architecture, understanding its benefits and throwing light on some leading trends in building a modern data framework. 

Table of Contents

The Key Components and Features of Modern Data Architecture

  • Data Storage and Management

Modern data architectures use a combination of diverse data storage and management technologies to meet unique analytics and data processing requirements. A fundamental component is a data lake, a centralized storage repository that holds large amounts of both unstructured and structured data. Data warehouses, proficient in handling structured data, play a pivotal role in analytical purposes. The emerging concept of a data lakehouse integrates the strengths of both data lakes and data warehouses. Also, the more focused data marts built for an organization’s specific business unit contribute significantly to data accessibility. Additionally, cloud-based databases, encompassing relational and NoSQL options, contribute to modern data architectures, offering versatility and improving scalability.  

Modern data architectures also offer seamless compatibility with cloud-native and hybrid environments, allowing enterprises to leverage the benefits of cloud services while maintaining the flexibility and interoperability of on-premises infrastructure. 

The core objective of modern data architecture is to ensure data flows freely for easy accessibility and consumption across the enterprise. 

  • Data Processing and Analytics

A modern data architecture is designed to handle the varied data processing needs of an enterprise – from batch processing to stream processing and real-time analytics. Modern data architectures support various cloud-based analytics solutions like Apache Flink and Apache Spark, enabling enterprises to scale their analytics capabilities as needed. 

Additionally, the self-serve capabilities of modern data architecture empower non-technical users to independently access, analyze, and derive insights from data without assistance from IT or data specialists.  

  • Data Governance

A modern data architecture enables metadata management, data lineage, data security and access control, regulatory compliance, and all other fundamentals of establishing a robust data governance framework. These effective data governance mechanisms ensure the organization maintains data quality, integrity, and security, for reliable insights.

  • Cost-effectiveness

The pay-as-you-scale flexibility of modern data architectures makes them cost-effective. As these architectures do not require ETL (Extract, Transform, and Load) and data movement, data management gets reduced.

3 Quintessential Elements of a Modern Data Architecture

1. High Data Quality

Data quality management is critical to the success of modern data architecture. For this configuring the technologies governing the data architecture appropriately is important. Continuous cleansing and transformation processes ensure consistent and high-quality data. Data quality tools and frameworks can automate the data cleansing processes and ensure data remains accurate, consistent, and reliable over time. 

2. Robust Data Strategy 

Data strategy is the foundation of a successful modern data architecture. Data engineers and IT professionals with a sound understanding of both business and data requirements can formulate effective data strategies. 

3. Alignment with Business Goal  

Building a data architecture that is aligned with the organization’s business goal is imperative for strategic success. Organizations can maximize the value of their data assets, adapt to changing market conditions, and experience long-term sustainability. 

Leading Trends in Building a Modern Data Architecture

As per the latest big data analytics trends, here are two emerging modern data architectures that address the common constraints of data lakes and legacy data warehouses: 

Data Mesh System: The decentralized architecture of a data mesh system is gaining rapid popularity, with data democratization and accessibility being the key drivers of this emerging trend. This architecture treats data as a product and involves distributed domain-oriented data ownership, opening new opportunities for the adoption of AI/ML use cases at scale and data innovation.  

The global data mesh market size, currently valued at USD 1.2 billion, is projected to reach $2.5 billion by 2028. 

Data Fabric System: This type of integrated data architecture is designed to improve enterprise-wide data accessibility through the adoption of automation and artificial intelligence for the extraction and integration of data sets. Data from diverse sources such as on-premises data systems, cloud-based systems, IoT devices, and others can be integrated and accessed using data fabric for holistic decision-making. Semantic knowledge graphs, active metadata management, and embedded machine learning are some of the technologies required to design a Data fabric that enhances collaboration significantly. 

Gartner identifies data fabric data management design pattern that empowers business users to consume data more easily amongst the top data and analytics trends. 

Final Thoughts

In the current competitive business landscape, modern data architecture is imperative to support a modern enterprise’s daily operations and address advanced analytics needs. While traditional on-premises data systems remain relevant, integrating newer structures on these legacy foundations poses challenges.

Adopting a modern, cloud-based foundation to support modern data architecture coupled with appropriate data tooling, enables the organization to unlock strategic insights. Modern data architecture offers numerous benefits in storing diverse data types, scalability, and minimizing latency in analytics, enabling enterprises to harness massive data volumes for enhanced decision-making. Moving beyond a one-size-fits-all approach, a careful selection of the right tools and techniques can help enterprises gain maximum value out of their modern data architectures and ensure alignment with their broader business objectives

If you are looking to build a modern data architecture on Azure Cloud or other cloud platforms using tools such as Databricks and Snowflake, get in touch with us to begin your journey on the right path. 

Author

Deepika Jayakodi
Deepika Jayakodi is a Data Architect at KANINI, bringing on board her decade-long expertise in Data Analytics, Warehousing, Business Intelligence, and Solutioning. She is an expert in project management, particularly in the US Healthcare, BFSI, and Manufacturing sectors. Deepika’s passion lies in architecting cloud data pipelines to deliver intelligent end-to-end solutions, demonstrating strategic implementation and analytical prowess.
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