- Terminology (A to D)
- AI Capability Control
- Asset Performance
- Bayes Theorem
- Big Data
- Chatbot: A Beginner’s Guide
- Computational Thinking
- Computer Vision
- Confusion Matrix
- Convolutional Neural Networks
- Data Fabric
- Data Storytelling
- Data Science
- Data Warehousing
- Decision Tree
- Deep Learning
- Deep Reinforcement Learning
- Diffusion Models
- Digital Twin
- Dimensionality Reduction
- Terminology (E to K)
- Edge AI
- Emotion AI
- Ensemble Learning
- Ethical Hacking
- Explainable AI
- Federated Learning
- Generative AI
- Generative Adversarial Network
- Generative vs. Discriminative
- Gradient Boosting
- Gradient Descent
- Few-Shot Learning
- Image Classification
- IT Operations (ITOps)
- Incident Automation
- Influence Engineering
- K-Means Clustering
- K-Nearest Neighbors
- Terminology (L to Q)
- Terminology (R to Z)
Table Of Contents
Often associated with artificial intelligence (AI) and machine learning (ML), a data fabric is one of the main tools for converting raw data into business intelligence.
But what exactly is a data fabric?
A data fabric is an architecture and software that offers a unified collection of data assets, databases, and database architectures within an enterprise. It facilitates the end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems.
Data fabrics have become more important as major developments continue to take place with the hybrid cloud, internet of things (IoT), AI, and edge computing. This has caused a massive increase in big data, which means organizations have even more to manage.
To deal with this big data, companies must focus on the unification and governance of data environments, which has posed several challenges like data silos, security risks, and bottlenecks in decision making. These challenges are what have led to data management teams adopting data fabric solutions, which help unify data systems, strengthen privacy and security, improve governance, and provide more data accessibility to workers.
Data integration leads to more data-driven decision-making, and while enterprises have historically used different data platforms for specific aspects of the business, data fabrics enable the data to be viewed more cohesively. All of this leads to a better understanding of the customer lifecycle, and it helps establish connections between data.
What’s the Purpose of a Data Fabric?
Data fabrics are used to establish a unified view of the associated data, which facilitates access to information regardless of its location, database association, or structure. Data fabrics also simplify analysis with AI and machine learning.
Another purpose of a data fabric is to facilitate application development since it creates a common model for accessing information separate from the traditional application and database silos. These models provide better information access, but they also improve efficiency by establishing a single layer where data access can be managed across all resources.
While there is not one single data architecture for a data fabric, it is often said that there are six fundamental components to this type of data framework:
Data Management: Responsible for data governance and security of data.
Data Ingestion: Brings cloud data together and identifies connections between structured and unstructured data.
Data Processing: Refines the data to ensure only relevant data is surfaced for data extraction.
Data Orchestration: A really important layer of the framework responsible for transforming, integrating, and cleansing data so it can be used across the business.
Data Discovery: Surfaces new ways to integrate data sources.
Data Access: Enables the consumption of data, ensures the right permissions for certain teams to comply with regulation, and helps surface relevant data through the use of dashboards and other data visualization tools.
Benefits of a Data Fabric
There are many business and technical benefits of data fabrics, such as:
Break Data Silos: Modern businesses often suffer from data silos as modern databases are associated with groups of applications and often grow as new ones are added to the enterprise. Data silos hold data of different structures and formats, but data fabrics can improve access to enterprise information and use collected data to improve operational efficiency.
Unite Databases: Data fabrics also help companies unite databases that are spread over a large area. They ensure that the location differences don’t result in barriers to access. Data fabrics simplify application development and can be used to optimize specific application data use without making data less accessible to other applications. They can also unify data that’s already moved into silos.
Single Way to Access Information: Data fabrics improve application portability and act as a single way to access information in both the cloud and data center.
Generate Insights at an Accelerated Pace: Data fabric solutions can easily handle complex datasets, which accelerates the time to insight. Their architecture enables pre-built analytics models and cognitive algorithms to process data at scale and speed.
Used by Technical and Non-Technical Users: Data fabrics are not only aimed at technical users. The architecture is flexible and can be used with a wide range of user interfaces. They can help build dashboards that can be understood by business executives, or their sophisticated tools can be used for data exploration by data scientists.
Best Practices for Implementing Data Fabrics
The global data market is constantly expanding, and there is strong demand in the space. Many companies look to implement a data architecture to optimize their enterprise data, and they follow some common best practices.
One such practice is to embrace a DataOps process model. Data fabric and DataOps aren’t identical, but according to a DataOps model, there is close connectivity between data processes, tools, and the users. By aligning users to rely on data, they can leverage the tools and apply insights. Without a DataOps model, users can struggle to extract enough from the data fabric.
Another best practice is to avoid turning the data fabric into just another data lake, which is a common occurrence. For example, a true data fabric cannot be achieved if you have all of the architectural components, such as data sources and analytics, but none of the APIs and SDKs. Data fabric refers to the architecture design, not a single technology. And some of the defining traits of the architecture are interoperability between components and integration readiness.
It’s also critical for the organization to understand its compliance and regulatory requirements. A data fabric architecture can improve security, governance, and regulatory compliance.
Since data is not scattered across systems, there is a smaller threat of sensitive data exposure. With that said, it's important to understand the compliance and regulatory requirements before implementing a data fabric. Different data types can fall under different regulatory jurisdictions. One solution is to use automated compliance policies that ensure data transformation complies with laws.
Data Fabric Use Cases
There are many different uses for a data fabric, but a few are highly common. One such common example is the virtual/logical collection of geographically diverse data assets to facilitate access and analysis. The data fabric is usually used for centralized business management in this case. Because the distributed line operations that collect and use the data are supported through traditional application and data access/query interfaces, there is a lot to be gained by organizations that have regional or national segmentation of their activities. These organizations often require central management and coordination.
Another major use case for data fabrics is the establishment of a unified data model following a merger or acquisition. When these take place, the database and data management policies of the previously independent organization often change, meaning it becomes more difficult to collect information across organizational boundaries. A data fabric can overcome this by creating a unified view of data that enables the combined entity to harmonize on a single data model.
Alex McFarland is a tech writer who covers the latest developments in artificial intelligence. He has worked with AI startups and publications across the globe.