Data Fabric

What is Data Fabric?

Data fabric acts as an integrated data layer, weaving together various processes, and accelerating insights. Serving as a cohesive fabric, it automates the seamless flow of data, encompassing ingestion, curation, discovery, preparation, and integration across diverse data silos. This holistic approach enhances agility and efficiency, fostering a unified and responsive data ecosystem.

How does data fabric work?

Data fabric operates by creating a semantic layer that expedites data and insights delivery through automated processes, enhancing agility. It interconnects disparate data sources, automating key tasks such as ingestion, curation, and integration. This cohesive approach forms a unified framework, fostering collaboration between business users and analysts in the data preparation process. By seamlessly weaving together various data silos, data fabric ensures a responsive and efficient ecosystem, accelerating the overall pace of data-driven decision-making.

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How is data fabric used?

Data fabric serves as a versatile solution with broad applications, transcending specific data processes, geographic locations, and platforms. Its inherent design enables seamless access and governance for various types of data, fostering inclusivity in data utilization. One of its key functionalities lies in enhancing data lake environments. By adding a supportive layer of information, data fabric diminishes the necessity for intricate point-to-point integrations, streamlining accessibility for users and processes.

In its most impactful role, data fabric proves instrumental in optimizing AI capabilities across extensive datasets, irrespective of the diversity in data types or locations. It acts as a unifying force, ensuring faster, more robust, and more reliable outcomes in the realm of AI-driven analytics. This adaptability positions data fabric as a fundamental tool for organizations seeking efficiency and cohesion in managing and extracting insights from diverse data sources, contributing to more informed and agile decision-making processes.

What can data fabric do?

Data fabric serves a multifaceted role by abstracting infrastructure and dismantling the direct connection between data and specific systems. This crucial feature eradicates silos, promoting a more interconnected data landscape. Focused on automation, data fabric excels in the vital stages of data management—ingestion, curation, and integration. Automating these processes across diverse data sources simplifies the complexities of data analytics, making insights more accessible and actionable for businesses.

A key attribute of data fabric is its ability to minimize complexity by automating processes, workflows, and pipelines. This streamlining effect simplifies data deployment, enhancing operational efficiency. The technology's adaptability is evident in its capacity to accommodate various data types and sources, ensuring a comprehensive approach to data management. Data fabric emerges as a transformative force, providing organizations with the means to break down barriers, automate critical processes, and extract meaningful insights from their data assets, contributing significantly to agile decision-making and business success.

Data fabric use cases

  • 360-degree view of the customer: Data fabric demonstrates its versatility through various impactful use cases. Customer relations enables a comprehensive 360-degree view by identifying preferences, social circles, buying patterns, and order history. This facilitates companies in assessing customer satisfaction, predicting churn, and personalizing experiences, which are pivotal for sustained business success.
  • Internet of Things (IoT) analytics: Data fabric efficiently handles vast volumes of data from sensors, devices, and switches. It streamlines storage, processing, and access to IoT data through automation and machine learning, integrating seamlessly with data lakes. This capability ensures operational insights and supports analytics by streaming data from diverse platforms.
  • Real-time and advanced analytics: Data fabric's impact extends to real-time and advanced analytics, supporting pervasive applications such as fraud detection and risk management. It identifies patterns in near real-time through automation, curation, and intelligent processing, enhancing the agility and effectiveness of analytics applications. These diverse use cases highlight data fabric's ability to adapt to varied business needs, making it an invaluable tool for organizations seeking efficiency and insights across different domains.

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.