Until a very few years ago, the concept of Big Data seemed reserved for a few enterprise-class companies. Today, data generation takes on exponential curves, and any company in the manufacturing sector already finds itself or will soon find itself having to manage an incredible amount of data from the most disparate sources ever. As is always the case, with as complexity increases, techniques and strategies for management multiply.
Regarding the organization of business information, three approaches, both technological and organizational, can be identified that can support companies to manage it more efficiently. Let us now try to shed light on the three best known: Data Lake, Data Fabric and Data Mesh.
Managing enterprise data: an increasingly complex task
According to this statistic¹, data generated, managed or acquired globally has increased exponentially, from about 2 Zettabytes in 2010 to an expected 97 Zettabytes in 2025. To give an idea of the size of the phenomenon², 90 percent of the world’s present data was created in the last two years.
Traditional relational databases, once the main tool in data management, are encountering increasing difficulties in coping with such large amounts of information. Designed to handle structured data and relatively limited amounts of data (here we refer to differences of orders of magnitude between “traditional” data and Big Data), these systems are colliding with the new requirements for speed, variety and volume that characterize today’s scenario. In the face of this complexity, solutions such as Data Lake, Data Fabric, and Data Mesh, offer more flexible and scalable approaches.
Data lake: definition and fields of use
Data lakes are centralized repositories capable of storing large volumes of heterogeneous data in raw format. Their main advantage is their ability to store unstructured, semi-structured, and structured data, making them ideal for companies that need extensive flexibility in data management and greatly reducing the time and cost of ingestion and subsequent processing.
Data fabric: innovative data management
Data Fabric refers not to a technology solution but, rather, to an organizational and management approach in which to create a unified view of enterprise data, regardless of their location. It integrates different platforms and data sources, providing simplified access to data through a single interface. This approach facilitates integration, access, and analysis of data, moving the issue of source of trust from a purely technical level to a higher, organizational level: data is no longer centralized but orchestrated in the logical place where it is located.
Data Mesh: decentralization and Domain-Driven Design
A data mesh is a distributed architecture that organizes data in autonomous and decentralized domains, each with its own responsibilities for datamanagement, governance, and access. Data Mesh principles enable the integration, analysis and consumption of data to scale efficiently and effectively, leveraging the principles of the domain-oriented design (Domain-Driven Design), data federation and self-service computation. An approach, in short, that is no longer monolithic and allows for more agile use of data by dividing its management among multiple entities.
Data warehouse: the foundation for enterprise data retention
Data warehouses represent a traditional solution for storing and analyzing business data and still constitute, regardless of the strategy identified, a essential technological need for enterprises that need to manage large amounts of information. A data warehouse is a data management system designed to enable and support business intelligence activities, particularly structured analysis.
The system centralizes and consolidates data, creating consistency across types and formats. Data Lake, Data Fabric, and Data Mesh offer more flexible and scalable approaches, but they do not replace Data Warehouses. The popular trend today is to use these new models in a hybrid context, integrated with existing data warehouses, to build a more robust and versatile data management ecosystem.
Data lake, Data fabric, Data Mesh: which is the best choice?
The choice between Data Lake, Data Fabric, and Data Mesh depends on various factors, including data volume, variety of data sources, analysis needs, and organizational structure of the company.
Let us see, in brief, what are the key factors that should guide the choice.
Volume and variety of business data
- Data Lake: ideal for Large volumes of data, especially if unstructured or semi-structured. Perfect for storing raw data for future processing or analysis.
- Data Fabric: suitable for companies that need to integrate and access data from diverse sources, including legacy and cloud systems.
- Data Mesh: especially suitable for realities with a product-oriented and decentralized corporate culture, where different units manage and information independently.
Analysis needs and data access:
- Data lakes provide a basis for advanced analytics and machine learning, but require specific skills to extract value from raw data.
- Data Fabric facilitatesreal-time analysis through the ability to connect disparate data seamlessly and dynamically.
- Data Meshs promote an autonomous approach to analysis, with teams managing and analyzing data within their specific domains.
Technological maturity and organizational culture:
- Data lakes require a robust IT infrastructure and a corporate culture inclined towardtechnological innovation.
- Data Fabric are ideal for companies that have a strong need for data integration and centralized governance.
- Data Mesh, finally, is best for companies with an agile organizational structure and a strong emphasis on collaboration between departments.
Different approaches for different needs
As we have seen, Data Lake, Data Fabric, and Data Mesh offer diverse approaches to data management. While Data Warehouses continue to play a crucial role in data analysis, these new solutions offer more flexibility and opportunities for innovation, starting with the organizational approach.
Discover with us the best way to manage your business data.
