Data apps built on a data fabric.
What is a
data fabric?
A data fabric is a software architecture that integrates data from disparate sources across an organization's on-premises and cloud environments. It provides a unified view of all data, making it easier to find, access, and analyze.
A data fabric typically consists of the following components:
A data catalog: This stores metadata about all the data in the fabric, such as its location, schema, and lineage.
A data integration layer: This layer connects the different data sources and transforms the data into a common format.
A data governance layer: This layer enforces policies and controls over the data, such as who can access it and how it can be used.
A data analytics layer: This layer provides tools for analyzing the data and generating insights.
Data fabrics are used to solve a variety of data challenges, such as:
Data silos: Data fabrics can break down data silos by connecting different data sources and providing a unified view of all data.
Data quality: Data fabrics can help improve data quality by providing tools for cleansing and validating data.
Data governance: Data fabrics can help enforce data governance policies and controls to ensure that data is used in a compliant and ethical manner.
Data security: Data fabrics can help protect data by providing encryption and access controls.
Data analytics: Data fabrics can help organizations make better decisions by providing insights from their data.
Data fabrics are a powerful tool that can help organizations to better manage their data and derive insights from it. They are becoming increasingly important as organizations adopt hybrid cloud and multi-cloud architectures.
Here are some of the benefits of using a data fabric:
Increased data visibility and accessibility: A data fabric makes it easier to find and access data from all across the organization, regardless of where it is stored. This can help to improve decision-making and collaboration.
Improved data quality: A data fabric can help to improve data quality by providing tools for cleansing and validating data. This can help to reduce the risk of errors and improve the reliability of data-driven insights.
Enhanced data governance: A data fabric can help to enforce data governance policies and controls to ensure that data is used in a compliant and ethical manner. This can help to protect the organization from data breaches and other security risks.
Accelerated time to insights: A data fabric can help to accelerate the time it takes to get insights from data by providing a unified view of all data and making it easier to access and analyze. This can help organizations to make better decisions more quickly.
Increased agility and flexibility: A data fabric can help organizations to be more agile and flexible by making it easier to adapt to changes in data sources and requirements. This can help organizations to stay ahead of the competition and meet the needs of their customers.
Orbyfy’s data apps are built on a data fabric architecture.
Creating connected 3D data networks.
Fabric+ value
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Immediate payback in speed and time with positive ROI after 1st project: last-copy low-code data integration.
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Investment in model AI-ready stack: data fabric, data mesh, pseudo-blockchain governed data.
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Factor 10x speed: enterprise technology transformation initiatives and application development.
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Exponential knowledge growth: data to insights to decisioning across the enterprise.
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Accelerate AI-led innovation & business models immediately: ChatGPT, LLMs, large-scale AI connected to all enterprise data.
Every layer of a data fabric adds value.
A data fabric is 3D data, not just data stored in rows & columns.
What’s cool about this approach?
Each data point can be represented as a vector: (row, column, metadata) or (x, y, z) for all the math nerds out there. This forms a 3D data network at the cellular data point level.
What does it mean?
It means that data is virtualized, decoupled from applications, and persisted such that you avoid the constant need for point-to-point integrations or enterprise service bus layers. It means you can start to spend more of your time transforming data to insights, rather than data management alone.
Key differentiators
Agility: A data fabric architecture can provide greater agility than an API management tool or enterprise service bus middleware because it can handle a wider range of data types and formats. This means that data can be accessed and analyzed more quickly, making it easier to develop and deploy applications and models.
Flexibility: A data fabric architecture can also offer greater flexibility than API management tools or enterprise service bus middleware, as it can integrate data from a variety of sources, such as databases, data lakes, and streaming sources. This enables organizations to be more responsive to changing business requirements and to scale up or down as needed.
Scalability: Data fabric architecture can handle large volumes of data and scale up or down as needed, making it easier to handle the demands of large-scale AI applications, including large language models.
Unified View of Data: A data fabric architecture provides a unified view of data across the enterprise, allowing teams to access and analyze data from a variety of sources more easily. This can enable better collaboration and faster decision-making.
Advanced Analytical Capabilities: A data fabric architecture can use advanced analytical techniques such as machine learning and natural language processing to identify patterns and relationships within the data. This can enable more sophisticated analysis and modeling, leading to more accurate predictions and better business outcomes
The value is endless.
Data apps built on a data fabric not only manage data, but create Functions-as-a-Service (FaaS), interconnected conditional logic flows based on data triggers, rules-engines, create pseudo-blockchain based smart contracts, and more.
