What is Data Fabric?
Data fabric is a design concept that serves as an integrated layer (fabric) of data and connecting processes. It accelerates insights by automating ingestion, curation, discovery, preparation, and integration across data silos.
How does data fabric work?
Data fabric creates a semantic layer that accelerates the delivery of data and insights by automating key processes, increasing agility while engaging business users and analysts in the data preparation process.
How is data fabric used?
Data fabric is not specific to data process or use, nor geographic locations or platforms. Its very design ensures all types of data can be easily accessed and governed. It adds a layer of supporting information to data lake environments, reducing the need for point-to-point integration for certain users and processes.
At its most useful, data fabric facilitates faster, more robust, and more reliable results when leveraging AI capabilities across large amounts of data, regardless of how many types or locations.
What can data fabric do?
Data fabric abstracts infrastructure, breaking the direct tie between data and specific infrastructure and so breaking down silos. It focuses on automating the process of data ingestion, data curation, and the integration of diverse data sources, simplifying data analytics and insights for business success. It minimizes complexity by automating processes, workflows, and pipelines streamlining data to simplify deployment.
Data fabric use cases
- 360-degree view of the customer: Data fabric helps companies identify customers’ likes, dislikes, circle of friends, buying patterns, and past orders. It helps companies identify customer satisfaction, predict churn, and personalize experiences critical for business success.
- Internet of Things (IoT) analytics: Data fabric offers the ability to efficiently store, process, and access large volumes of IoT data from sensors, devices, and switches through automation and machine learning technologies. It enables analytics by steaming data from other data platforms and integrates with data lakes to deliver operational insights.
- Real-time and advanced analytics: Data fabric supports pervasive analytics through automation, curation, and intelligence that is used for fraud detection, risk management, and applications. These benefit from the use of additional data signals to identify patterns in near real time.
AI and data fabric
- AI in Action: Artificial intelligence (AI) enables machines to perform tasks that resemble those of human intellect. It includes both general AI and narrow AI, which mimic various aspects of human cognitive function. AI can learn, make decisions, recognize data patterns, and analyze them.
- Architecting Data Harmony via Architecture: Data fabric is an architectural concept that is changing how businesses handle data by giving employees a common view throughout the company. Data silos are eliminated, allowing for more effective data access, transportation, and analysis. Strong security and governance, interconnection, flexibility, and a uniform architecture are important characteristics.
- AI and data go hand in hand: AI systems require high-quality data for training and learning. Data fabric ensures that different datasets are seamlessly integrated and easily available to AI algorithms. The data fabric's real-time capabilities complement the AI applications' dynamic requirements.
- Real-time Accuracy and Scalability: The need for real-time data processing brings together the strengths of data fabric and artificial intelligence. AI's requirement for the most recent information is complemented by the data fabric's real-time capabilities. In addition, scalability is a common need that addresses growing computing demands and data volumes.
- Realizing Data Potential: The whole potential of an organization's data assets can be realized by combining AI with a strong data fabric. AI applications can produce insights, forecasts, and automation because of this cooperative approach, which makes efficient use of data possible. Organizations are better positioned for success in the data-driven environment when AI and data fabric are combined to support a comprehensive and flexible approach to data management.
HPE and data fabric
Hewlett Packard Enterprise (HPE) advances high-performance hybrid analytics with HPE Ezmeral Data Fabric, offering a transformative approach that doesn't involve migrating or limiting data sets. The Data Fabric unifies data across various sources like data lakes, files, objects, streams, and databases, creating a cohesive data infrastructure and file system. This integration extends to existing files, objects, streams, and databases, breaking down data silos and providing a unified technology base, security framework, and management system.
A distinctive feature of HPE's approach is the creation of edge-to-cloud topologies accessed through a single global namespace, ensuring simplified data access from any application or interface, regardless of data location. The persistence of a unified data store simplifies coding and data analytic models, offering cross-protocol data access via native S3, NFS, POSIX, REST, HDFS, and container storage interface (CSI) APIs. HPE Ezmeral Data Fabric emerges as a comprehensive solution, facilitating seamless integration, accessibility, and management of diverse data sources for organizations seeking enhanced efficiency and agility in their analytics processes.