Data Fabric

We specialise in integrating and managing data across heterogeneous sources using cutting-edge Data Fabric and Azure Fabric technologies. Discover how our expertise can streamline your data operations and drive impactful business outcomes.

Enhanced Data Management

Leverage AI, ML, and active metadata to automate data management tasks, reducing complexity and increasing productivity.

Future-Proof Data Architecture

Build a scalable, resilient data architecture that adapts to evolving business needs and technological advancements.

Seamless Data Integration

Achieve flexible and reusable data integration pipelines with our Data Fabric solutions, ensuring consistent and real-time data access.

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Technology Overview

We help organisations adopt and implement Data Fabric solutions. Our support overcomes challenges with data silos, integration complexities, and a lack of internal expertise.

Our team of seasoned professionals brings the expertise and capacity needed to design, deploy, and manage Data Fabric architectures. By leveraging cutting-edge technologies such as AI, ML, and active metadata, we enable businesses to achieve flexible, reusable, and automated data integration pipelines.

FAQ

Explore the insights and clarity that our services bring to the world of data. Whether you're navigating the complexities of integration or ensuring the best data quality, we're here to answer your questions.

What is Data Fabric and why is it important for my organisation?

Data Fabric is an emerging data management design that enables flexible, reusable, and augmented data integration pipelines. It leverages technologies such as AI, ML, and active metadata to automate data integration tasks, reduce complexity, and improve data accessibility and sharing across heterogeneous data sources. For organisations struggling with data silos and integration complexities, adopting Data Fabric can significantly enhance data management efficiency and support overburdened data engineering teams

How does Data Fabric differ from traditional data integration methods?

Unlike traditional data integration methods that often involve manual processes and siloed architectures, Data Fabric provides a unified and automated approach to data integration. It combines various data integration styles, such as ETL (Extract, Transform, Load) and data virtualisation, within a single framework. Additionally, Data Fabric utilises active metadata, knowledge graphs, and semantics to augment and, in some cases, automate data integration design and delivery, making it more scalable and adaptable to evolving business needs

What are the key components and technologies involved in implementing a Data Fabric?

Implementing a Data Fabric involves several key components and technologies:

Active Metadata: Facilitates automation and enhances data integration processes.
Knowledge Graphs and Semantics: Enable better data understanding and contextualisation.
Data Integration Styles: Combines ETL, data virtualisation, and other integration methods.
AI and ML: Enhance automation and decision-making capabilities.
DataOps and Team Structures: Essential for managing the data fabric and ensuring it meets business requirements.

Featured Use Cases

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Automating Data Management Tasks

Context & Need: Data management tasks such as integration, cleansing, and transformation are often manual and error-prone. The growing volume and variety of data make it hard for data engineering teams to keep up. Automation is needed to reduce workload and improve data quality.

Practical Application: Data Fabric solutions can automate repetitive data management tasks using active metadata, knowledge graphs, and AI/ML. For instance, a financial services company can automatically integrate and cleanse data from various financial systems, ensuring accurate and up-to-date information for compliance reporting and risk management.

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Enabling a 360-Degree Customer View

Context and Need: Achieving a comprehensive view of customers is crucial for sectors like retail, healthcare, and finance. Disparate data sources and siloed systems often hinder this goal, leading to fragmented insights and suboptimal customer experiences.

Practical Application: Data Fabric enables the integration of customer data from various sources such as CRM systems, social media, and transactional databases. This unified data layer provides a holistic view of each customer, allowing for personalised marketing and improved customer service. For example, a healthcare provider can integrate patient data from electronic health records and wearable devices to enhance patient care and operational efficiency.

Enhancing
Real-Time
Analytics

Context and Need: Organisations increasingly rely on real-time analytics for swift decision-making. Traditional systems often fail to provide the necessary speed and integration, leading to delayed insights and missed opportunities. There is a growing need for solutions that can handle real-time data processing from diverse sources.

Practical Application: Implementing Data Fabric allows organisations to leverage AI/ML and in-memory processing for real-time data integration and analytics. This enables seamless integration of data from sources like IoT devices and transactional systems. For example, a retail company can integrate point-of-sale data with online shopping behavior in real-time, adjusting inventory and marketing campaigns based on current trends.