Member post originally published on the Mia-Platform blog by Mia-Platform

In the last century, data has become the lifeblood of every organization, ranging from e‑commerce giants to healthcare providers and government agencies. Collecting and managing this data effectively can give organizations valuable insights to aid decision‑making. However, this has been proven to be a daunting task.

As important as data is, CIOinsight reports that only 10% of organizations feel that their company excels in data analytics management. Recognizing this significant data utilization gap, organizations have embraced modern data architectures to bridge the divide.

Data architectures are the structured frameworks and systems that define how data is organized, integrated, and accessed within an organization. The architecture sets the blueprint and establishes guidelines for data and how it flows through data storage systems.

This article discusses the evolution of data architecture, its underlying principles, and the advantages of adopting modern data architectures for effectively managing organizational data.

The evolution of data architecture

Over the years, data architecture has evolved to adapt to the growing needs of businesses. A notable transformation discussed in this section is the shift in data architecture from logical warehouses to data fabrics.

The Logical Warehouse

Logical warehouses, also known as data warehouses, have been the foundation of data management for decades. These data warehouses are central repositories designed to store data from different sources like transactional systems, application log files, relational databases, etc., providing a unified view of the information.

In general, logical warehouses use Extract, Transform, Load (ETL) processes to extract data from source systems, transform it to ensure consistency, and load it into the warehouse. Logical warehouses are solely intended to perform queries and analysis and often contain large amounts of historical data.

Challenges of Logical Warehouses

While logical warehouses served their purpose, they faced several challenges as data volumes increased. Some of the key limitations included:

Data Lakes

In response to the data warehouse challenges, data lake architecture was introduced in 2010. Although the data lake architecture is very similar to data warehouses, they differ because data lakes also cater to semi‑structured and unstructured data.

Data lakes’ ability to store large amounts of data in its natural or raw format helps us to:

Challenges of Data Lakes

This open format feature of the data lakes made it more popular than data warehouses. However, data lakes came with challenges as data ingested without strict standardization led to inconsistencies in the database. Additionally, the data stored in data lakes require significant transformation and integration efforts before it can be used for analysis which can be complex and time‑consuming.

Data Mesh

Data Mesh architecture is an emerging approach that provides an alternative to the central data lake architecture. Data Mesh, coined by Zhamak Dehghani in 2019, is a decentralized data architecture that organizes data by a specific business domain.

By introducing domain‑oriented data ownership, domain teams become accountable for their data and products, improving data quality and governance.

Traditional data lakes often encounter challenges related to scalability and performance when handling large volumes of data. However, data mesh architecture solves these scalability issues through its decentralized and self‑serve data infrastructure.

With each domain having the autonomy to choose the technologies and tools that best suits their needs, data mesh allows teams to scale their data storage and processing systems independently.

Data Fabrics

Data Fabric is an integrated data architecture that is adaptive, flexible, and secure. It is an architectural approach and technology framework that addresses data lake challenges by providing a unified and integrated view of data across various sources.

Data Fabric allows faster and more efficient access to data by extracting the technological complexities involved in data integration, transformation, and movement so that anybody can use it.

To understand the difference between the data mesh and data fabric architecture approach to achieve data quality and what data paradigm to adopt, check out this Mia‑Platform article Data Mesh vs Data Fabric: a tale of two new data paradigms.

Modern data architecture principles

According to Dataversity, data architecture principles refer to a collection of policies regulating the enterprise data framework and the operational rules for gathering, integrating, and managing data assets. These principles help us to create a consistent, reliable, and efficient data architecture that aligns with our organization’s goals and objectives.

To effectively leverage data as a competitive asset, here are some of the common modern data architecture principles to follow:

Characteristics of modern data architecture

This section discusses some of the characteristics of modern data architecture:

Benefits of modern data architecture

After discussing modern data architectures, and their characteristics, this section discusses the benefits of modern data architectures to businesses and organizations.


The evolution of data architecture has been driven by the growing importance of data in organizations. From traditional data warehouses to modern data mesh and data fabric approaches, these architectures addressed specific challenges and brought new opportunities.

By embracing modern data architectures, organizations can benefit from improved data quality and comprehensive data insights to unlock the full potential of their data and stay competitive in today’s world.

An amazing cohabitation of the two prominent modern data architectures, data mesh and data fabric is Mia‑Platform Fast Data. To understand more about Mia‑Platform Fast Data, read this article and download the free infographic.