Thanks to the momentum in digital transformation and advancements in new technologies, organizations have realized that data analysis and processing are their most vital resources.

Since the 2010s, the term Big Data was coined to describe the reality many companies faced where data management became a necessity, regardless of their size or sector. Prior to this environment, data was structured and stored in relational database systems grouped in high-cost large servers. This traditional approach made it difficult to work with unstructured or semi-structured data agilely, and was used only for system implementation or, in a few cases, for process automation.

Due to the push in digital transformation and advances in new technologies, organizations have come to understand that data analysis and processing are their most critical resources, as it provides a competitive advantage over other companies in the sector. The need for policies, models, and regulations that optimize this information analysis is what will give any company that differential capacity, but achieving this requires a good data architecture.

Main Data Architectures models:

There are different types of data architectures, and in this article, we will focus on the three most well-known: data warehouse, data lake, and data mesh. These are the most used by companies to manage and leverage the competitive advantage that good information use provides. Each one encompasses different characteristics and advantages that, depending on the company’s needs, may be more or less suitable:

1. Data Warehouse:This was one of the first types of data architecture, emerging in the 1980s to aid business decision-making through the transformation of operational data. Its structure resembles a large centralized warehouse where a vast amount of information is compiled and processed. Its advantages include:

• • Enables the integration of data from different sources and formats, thanks to its structure and design.
  • Provides relevant historical information for identifying past patterns or behaviors, useful for present and future decision-making.

However, this storage methodology has higher operational costs, is more complex to use, as data must first be structured, and requires constant maintenance.

Nevertheless, the Data Warehouse has laid the foundation for the development of new data architectures like Data Lake and Data Mesh.

2. Data Lake: This data architecture, created in 2010, is a centralized repository for structured and unstructured data. Over the years, it has evolved into different models like Cloud Data Lake and Data LakeHouse. The latter model emerges as a hybrid of the two previously mentioned and combines the management and analysis capabilities of the Data Warehouse with the flexibility and scalability of the Data Lake. Its advantages are:

• • Improved data governance: this methodology creates a standardized open space allowing greater control over security, consolidating all database resources, and improving access to all information.
  • Greater cost-effectiveness: this data architecture model separates processing resources from storage, allowing for savings and optimized computing power.
  • Allows storing and analyzing any type of data, whether structured, semi-structured, or unstructured, at any scale.

The Data LakeHouse is the evolution of data repositories by combining Data Lake and Data Warehouse. This structure, combined with a powerful analysis tool, enables the use of machine learning techniques like predictive analysis or recommendation engines.

3. Data Mesh: This modern data architecture is the main driver of data democratization. Its decentralized approach allows anyone given access to analyze and visualize information immediately. Its advantages are:

• • Increased collaboration among teams: this methodology promotes collaborative work among different organizational departments through data exchange on different interfaces.
  • Improved data quality: by dividing tasks among various teams, greater attention is paid to the analysis and management of information.
  • Greater transparency of information: Data Mesh fosters clarity in information by establishing key points in data governance.

This storage methodology is essential for all data-driven companies, as its data access policy allows for higher quality and efficiency in the information analytics process.

Conclusion:

As we have seen, having a structured and updated data architecture will enable functional design and effective data management. All this will allow for better analysis of trends, increased efficiency of organizational processes, and, of course, optimized decision-making.

Knowing the needs within the organization is crucial when choosing the type of architecture to implement. At PUE, we offer a Consulting service from a Data Experts perspective, which studies and analyzes all factors affecting the company to provide you with the best service in data management, analysis, and migration.

Companies like Telefónica and Carrefour already rely on our 100% certified teams in technology, so don’t hesitate to check out our services!