KNOWLEDGE FABRIC
Navigating the Future with Knowledge Fabric
Navigating the Future with Knowledge Fabric
Knowledge fabric is an emerging data management design that aims to provide flexible, reusable and augmented data integration across various data environments and platforms. Knowledge fabric leverages artificial intelligence, metadata and cloud-native technologies to deliver reliable and accessible data to data consumers.
Knowledge fabric is an emerging data management design that aims to provide flexible, reusable and augmented data integration across various data environments and platforms. Knowledge fabric leverages artificial intelligence, metadata and cloud-native technologies to deliver reliable and accessible data to data consumers.
Knowledge fabric is an emerging data management design that aims to provide flexible, reusable and augmented data integration across various data environments and platforms. Knowledge fabric leverages artificial intelligence, metadata and cloud-native technologies to deliver reliable and accessible data to data consumers.
What is Data Fabric?
According to Gartner, data fabric is “a design concept that serves as an integrated layer (fabric) of data and connecting processes. Data fabric leverages ongoing analytics on existing, discoverable, and inferred metadata resources to facilitate the creation, deployment, and utilization of integrated and reusable data across diverse environments, including hybrid and multi-cloud platforms.
Data fabric is not a single technology or product, but a combination of data platforms, technologies and services that work together to provide a unified and consistent data layer for data consumers. Data fabric abstracts the complexity of data integration and management, and enables data access and sharing across different data domains, sources, types and locations. Data fabric also leverages artificial intelligence and machine learning to automate and augment data engineering, data quality, data discovery, data curation and data governance tasks. Data fabric aims to deliver trusted, relevant and timely data to support various data and analytics use cases, such as business intelligence, data science, machine learning, artificial intelligence and data-driven decision making.
According to Gartner, data fabric is “a design concept that serves as an integrated layer (fabric) of data and connecting processes. Data fabric leverages ongoing analytics on existing, discoverable, and inferred metadata resources to facilitate the creation, deployment, and utilization of integrated and reusable data across diverse environments, including hybrid and multi-cloud platforms.
Data fabric is not a single technology or product, but a combination of data platforms, technologies and services that work together to provide a unified and consistent data layer for data consumers. Data fabric abstracts the complexity of data integration and management, and enables data access and sharing across different data domains, sources, types and locations. Data fabric also leverages artificial intelligence and machine learning to automate and augment data engineering, data quality, data discovery, data curation and data governance tasks. Data fabric aims to deliver trusted, relevant and timely data to support various data and analytics use cases, such as business intelligence, data science, machine learning, artificial intelligence and data-driven decision making.
According to Gartner, data fabric is “a design concept that serves as an integrated layer (fabric) of data and connecting processes. Data fabric leverages ongoing analytics on existing, discoverable, and inferred metadata resources to facilitate the creation, deployment, and utilization of integrated and reusable data across diverse environments, including hybrid and multi-cloud platforms.
Data fabric is not a single technology or product, but a combination of data platforms, technologies and services that work together to provide a unified and consistent data layer for data consumers. Data fabric abstracts the complexity of data integration and management, and enables data access and sharing across different data domains, sources, types and locations. Data fabric also leverages artificial intelligence and machine learning to automate and augment data engineering, data quality, data discovery, data curation and data governance tasks. Data fabric aims to deliver trusted, relevant and timely data to support various data and analytics use cases, such as business intelligence, data science, machine learning, artificial intelligence and data-driven decision making.
The key aspects of data fabric
Data fabric has several key aspects that make it a powerful and flexible data management solution, such as
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
Data platform agnostic
Data fabric can work on different deployment platforms and with different data processing methods, such as relational, non-relational, cloud, on-premises, batch, streaming, etc. Data fabric does not require moving or copying data from its original sources, but rather connects to them and accesses them in place or supports their consolidation where appropriate.
Data service oriented
Data fabric provides a set of data services and APIs that enable data consumers to access, query, analyze and visualize data from various data sources and platforms. Data fabric also supports data orchestration, data transformation, data enrichment and data delivery across the data fabric layer.
Data governance embedded
Data fabric incorporates data governance as a core component of its architecture, ensuring data quality, data security, data privacy and data compliance across the data fabric layer. Data fabric also leverages metadata, both existing and inferred, to provide data lineage, data catalog, data dictionary, data classification, data policy and data audit capabilities.
Data intelligence driven
Data fabric uses artificial intelligence and machine learning to automate and augment data management tasks, such as data engineering, data discovery, data curation, data integration and data optimization. Data fabric also uses continuous analytics to monitor, analyze and improve data performance, data reliability and data value
The technological journey towards data fabric
Data fabric is the result of the continuous innovation and advancement of data management technologies and practices in response to the changing data landscape and business needs. The following is a brief review of the major milestones and trends that preceded data fabric
Data warehouse
Data warehouse is one of the earliest and most widely used data management solutions, which involves extracting, transforming and loading (ETL) data from various data sources into a centralized repository that is optimized for analytical queries and reporting. Data warehouse provides a single source of truth for data and supports structured and semi-structured data. However, data warehouse also has some limitations, such as high cost, low flexibility, data silos, data latency, data quality issues and difficulty to handle unstructured and streaming data.
Data lake
Data virtualization
Data as a service
Data mesh
The technological journey towards data fabric
Data fabric is the result of the continuous innovation and advancement of data management technologies and practices in response to the changing data landscape and business needs. The following is a brief review of the major milestones and trends that preceded data fabric
Data warehouse
Data warehouse is one of the earliest and most widely used data management solutions, which involves extracting, transforming and loading (ETL) data from various data sources into a centralized repository that is optimized for analytical queries and reporting. Data warehouse provides a single source of truth for data and supports structured and semi-structured data. However, data warehouse also has some limitations, such as high cost, low flexibility, data silos, data latency, data quality issues and difficulty to handle unstructured and streaming data.
Data lake
Data virtualization
Data as a service
Data mesh
The technological journey towards data fabric
Data fabric is the result of the continuous innovation and advancement of data management technologies and practices in response to the changing data landscape and business needs. The following is a brief review of the major milestones and trends that preceded data fabric
Data warehouse
Data warehouse is one of the earliest and most widely used data management solutions, which involves extracting, transforming and loading (ETL) data from various data sources into a centralized repository that is optimized for analytical queries and reporting. Data warehouse provides a single source of truth for data and supports structured and semi-structured data. However, data warehouse also has some limitations, such as high cost, low flexibility, data silos, data latency, data quality issues and difficulty to handle unstructured and streaming data.
Data lake
Data virtualization
Data as a service
Data mesh
The technological journey towards data fabric
Data fabric is the result of the continuous innovation and advancement of data management technologies and practices in response to the changing data landscape and business needs. The following is a brief review of the major milestones and trends that preceded data fabric
Data warehouse
Data warehouse is one of the earliest and most widely used data management solutions, which involves extracting, transforming and loading (ETL) data from various data sources into a centralized repository that is optimized for analytical queries and reporting. Data warehouse provides a single source of truth for data and supports structured and semi-structured data. However, data warehouse also has some limitations, such as high cost, low flexibility, data silos, data latency, data quality issues and difficulty to handle unstructured and streaming data.
Data lake
Data virtualization
Data as a service
Data mesh
DataFab’s way of handling Data Management
Data Fabric, Data Mesh and Data Lake all aim to solve the challenges of managing and accessing data in complex, modern organizations. However, they take vastly different approaches, each with its own pros and cons. In DataFab we adopt the best of all worlds, while addressing their challenges in a Knowledge Fabric:
Data Fabric
Focus
A design concept that serves as an integrated layer of data and connecting processes. It utilizes continuous analytics over existing, discoverable, and inferred metadata assets to support the design, deployment, and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms
Key Aspects
Data platform agnostic, data service-oriented, data governance embedded, data intelligence-driven
Benefits
Flexible, reusable, and augmented data integration, abstracts complexity, supports real-time data access
Challenges
Complexity in implementation, requires metadata management, may not handle all data types
Our approach
DataFab secret sauce
Full Data Fabric inside plus:
-Composability for seamless implementation;
-Market leading Entity Resolution for metadata management and flexible schema
Data Fabric
Focus
A design concept that serves as an integrated layer of data and connecting processes. It utilizes continuous analytics over existing, discoverable, and inferred metadata assets to support the design, deployment, and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms
Key Aspects
Data platform agnostic, data service-oriented, data governance embedded, data intelligence-driven
Benefits
Flexible, reusable, and augmented data integration, abstracts complexity, supports real-time data access
Challenges
Complexity in implementation, requires metadata management, may not handle all data types
Our approach
DataFab secret sauce
Full Data Fabric inside plus:
-Composability for seamless implementation;
-Market leading Entity Resolution for metadata management and flexible schema
Data Mesh
Focus
An emerging data management paradigm that advocates for a decentralized and distributed approach to data ownership and governance. It treats data as a product and assigns data domains to different teams or units within an organization, who are responsible for creating, maintaining, and delivering data products to data consumers.
Key Aspects
Decentralized and distributed data ownership, data domain-based teams, data products, data agility, data collaboration.
Benefits
Increases data agility, collaboration, and trust, overcomes data silos, supports data innovation.
Challenges
Requires organizational change, may lead to data fragmentation, needs strong governance.
Our approach
DataFab secret sauce
Full Data Mesh inside plus:
-Composability empowering business units to maintain existing processes;
-Centralized Data Fabric negating data fragmentation and enforcing strong data governance.
Data Lake
Focus
A centralized repository for storing data from various data sources in its original format and granularity. It supports structured, semi-structured, and unstructured data, and enables data ingestion and storage at a lower cost and higher speed than traditional data warehouses
Key Aspects
Centralized repository, original data format, cost-effective storage, supports various data types, data ingestion
Benefits
Cost-effective storage, supports various data types, enables data ingestion at scale
Challenges
May lack data governance, data discovery challenges, may not handle real-time data well
Our approach
DataFab secret sauce
Out of box integration to existing or future data lakes, plus:
-Centralized Data Fabric enforcing strong data governance and supporting real-time data utilization;
-Market leading Entity Resolution for unparalleled data discovery
DataFab’s way of handling Data Management
Data Fabric, Data Mesh and Data Lake all aim to solve the challenges of managing and accessing data in complex, modern organizations. However, they take vastly different approaches, each with its own pros and cons. In DataFab we adopt the best of all worlds, while addressing their challenges in a Knowledge Fabric:
Data Fabric
Focus
A design concept that serves as an integrated layer of data and connecting processes. It utilizes continuous analytics over existing, discoverable, and inferred metadata assets to support the design, deployment, and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms
Key Aspects
Data platform agnostic, data service-oriented, data governance embedded, data intelligence-driven
Benefits
Flexible, reusable, and augmented data integration, abstracts complexity, supports real-time data access
Challenges
Complexity in implementation, requires metadata management, may not handle all data types
DataFab secret sauce
Full Data Fabric inside plus:
-Composability for seamless implementation;
-Market leading Entity Resolution for metadata management and flexible schema
Our approach
Data Fabric
Focus
A design concept that serves as an integrated layer of data and connecting processes. It utilizes continuous analytics over existing, discoverable, and inferred metadata assets to support the design, deployment, and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms
Key Aspects
Data platform agnostic, data service-oriented, data governance embedded, data intelligence-driven
Benefits
Flexible, reusable, and augmented data integration, abstracts complexity, supports real-time data access
Challenges
Complexity in implementation, requires metadata management, may not handle all data types
DataFab secret sauce
Full Data Fabric inside plus:
-Composability for seamless implementation;
-Market leading Entity Resolution for metadata management and flexible schema
Our approach
Data Mesh
Focus
An emerging data management paradigm that advocates for a decentralized and distributed approach to data ownership and governance. It treats data as a product and assigns data domains to different teams or units within an organization, who are responsible for creating, maintaining, and delivering data products to data consumers.
Key Aspects
Decentralized and distributed data ownership, data domain-based teams, data products, data agility, data collaboration.
Benefits
Increases data agility, collaboration, and trust, overcomes data silos, supports data innovation.
Challenges
Requires organizational change, may lead to data fragmentation, needs strong governance.
DataFab secret sauce
Full Data Mesh inside plus:
-Composability empowering business units to maintain existing processes;
-Centralized Data Fabric negating data fragmentation and enforcing strong data governance.
Our approach
Data Lake
Focus
A centralized repository for storing data from various data sources in its original format and granularity. It supports structured, semi-structured, and unstructured data, and enables data ingestion and storage at a lower cost and higher speed than traditional data warehouses
Key Aspects
Centralized repository, original data format, cost-effective storage, supports various data types, data ingestion
Benefits
Cost-effective storage, supports various data types, enables data ingestion at scale
Challenges
May lack data governance, data discovery challenges, may not handle real-time data well
DataFab secret sauce
Out of box integration to existing or future data lakes, plus:
-Centralized Data Fabric enforcing strong data governance and supporting real-time data utilization;
-Market leading Entity Resolution for unparalleled data discovery;
Our approach
The strengths and benefits of using data fabric
The strengths and benefits of using data fabric
Data fabric offers several strengths and benefits that make it a superior data management solution, such as:
Data fabric offers several strengths and benefits that make it a superior data management solution, such as:
Data agility
Data fabric enables data consumers to access and use data faster and easier, without being constrained by data location, format, type or platform. Data fabric also enables data producers to create and update data products more quickly and efficiently, without being burdened by data integration and management complexity.
Data reliability
Data fabric ensures data quality, data security, data privacy and data compliance across the data fabric layer, using data governance, metadata and artificial intelligence. Data fabric also ensures data performance, data availability and data scalability, using cloud-native, microservices-based, API-driven and elastic technologies.
Data value
Data fabric delivers data that is relevant, timely and actionable to data consumers, supporting various data and analytics use cases, such as business intelligence, data science, machine learning, artificial intelligence and data-driven decision making. Data fabric also delivers data that is reusable, shareable and discoverable to data producers, enabling data collaboration, data innovation and data monetization.
Data agility
Data fabric enables data consumers to access and use data faster and easier, without being constrained by data location, format, type or platform. Data fabric also enables data producers to create and update data products more quickly and efficiently, without being burdened by data integration and management complexity.
Data reliability
Data fabric ensures data quality, data security, data privacy and data compliance across the data fabric layer, using data governance, metadata and artificial intelligence. Data fabric also ensures data performance, data availability and data scalability, using cloud-native, microservices-based, API-driven and elastic technologies.
Data value
Data fabric delivers data that is relevant, timely and actionable to data consumers, supporting various data and analytics use cases, such as business intelligence, data science, machine learning, artificial intelligence and data-driven decision making. Data fabric also delivers data that is reusable, shareable and discoverable to data producers, enabling data collaboration, data innovation and data monetization.
Data agility
Data fabric enables data consumers to access and use data faster and easier, without being constrained by data location, format, type or platform. Data fabric also enables data producers to create and update data products more quickly and efficiently, without being burdened by data integration and management complexity.
Data reliability
Data fabric ensures data quality, data security, data privacy and data compliance across the data fabric layer, using data governance, metadata and artificial intelligence. Data fabric also ensures data performance, data availability and data scalability, using cloud-native, microservices-based, API-driven and elastic technologies.
Data value
Data fabric delivers data that is relevant, timely and actionable to data consumers, supporting various data and analytics use cases, such as business intelligence, data science, machine learning, artificial intelligence and data-driven decision making. Data fabric also delivers data that is reusable, shareable and discoverable to data producers, enabling data collaboration, data innovation and data monetization.
DataFab’s way of handling Data Management
Data Fabric, Data Mesh and Data Lake all aim to solve the challenges of managing and accessing data in complex, modern organizations. However, they take vastly different approaches, each with its own pros and cons. In DataFab we adopt the best of all worlds, while addressing their challenges in a Knowledge Fabric:
Focus
Key Aspects
Benefits
Challenges
DataFab secret sauce
Full Data Mesh inside plus:
-Composability empowering business units to maintain existing processes;
-Centralized Data Fabric negating data fragmentation and enforcing strong data governance.
Full Data Fabric inside plus:
-Composability for seamless implementation;
-Market leading Entity Resolution for metadata management and flexible schema.
Out of box integration to existing or future data lakes, plus:
-Centralized Data Fabric enforcing strong data governance and supporting real-time data utilization;
-Market leading Entity Resolution for unparalleled data discovery
Data Mesh
An emerging data management paradigm that advocates for a decentralized and distributed approach to data ownership and governance. It treats data as a product and assigns data domains to different teams or units within an organization, who are responsible for creating, maintaining, and delivering data products to data consumers.
Decentralized and distributed data ownership, data domain-based teams, data products, data agility, data collaboration.
Increases data agility, collaboration, and trust, overcomes data silos, supports data innovation.
Requires organizational change, may lead to data fragmentation, needs strong governance.
Knowledge Fabric
Data Fabric
A design concept that serves as an integrated layer of data and connecting processes. It utilizes continuous analytics over existing, discoverable, and inferred metadata assets to support the design, deployment, and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms .
Data platform agnostic, data service-oriented, data governance embedded, data intelligence-driven
Flexible, reusable, and augmented data integration, abstracts complexity, supports real-time data access
Complexity in implementation, requires metadata management, may not handle all data types .
Data Lake
A centralized repository for storing data from various data sources in its original format and granularity. It supports structured, semi-structured, and unstructured data, and enables data ingestion and storage at a lower cost and higher speed than traditional data warehouses
Centralized repository, original data format, cost-effective storage, supports various data types, data ingestion
Cost-effective storage, supports various data types, enables data ingestion at scale
May lack data governance, data discovery challenges, may not handle real-time data well
DATAFAB.AI
© 2025 DATAFAB.AI. All rights reserved.
DATAFAB.AI
© 2025 DATAFAB.AI. All rights reserved.
DATAFAB.AI
© 2025 DATAFAB.AI. All rights reserved.
DATAFAB.AI
© 2025 DATAFAB.AI. All rights reserved.
DataFab’s way of handling Data Management
Data Fabric, Data Mesh and Data Lake all aim to solve the challenges of managing and accessing data in complex, modern organizations. However, they take vastly different approaches, each with its own pros and cons. In DataFab we adopt the best of all worlds, while addressing their challenges in a Knowledge Fabric:
Focus
Key Aspects
Benefits
Challenges
DataFab secret sauce
Full Data Mesh inside plus:
-Composability empowering business units to maintain existing processes;
-Centralized Data Fabric negating data fragmentation and enforcing strong data governance.
Full Data Fabric inside plus:
-Composability for seamless implementation;
-Market leading Entity Resolution for metadata management and flexible schema.
Out of box integration to existing or future data lakes, plus:
-Centralized Data Fabric enforcing strong data governance and supporting real-time data utilization;
-Market leading Entity Resolution for unparalleled data discovery
Data Mesh
An emerging data management paradigm that advocates for a decentralized and distributed approach to data ownership and governance. It treats data as a product and assigns data domains to different teams or units within an organization, who are responsible for creating, maintaining, and delivering data products to data consumers.
Decentralized and distributed data ownership, data domain-based teams, data products, data agility, data collaboration.
Increases data agility, collaboration, and trust, overcomes data silos, supports data innovation.
Requires organizational change, may lead to data fragmentation, needs strong governance.
Data Fabric
A design concept that serves as an integrated layer of data and connecting processes. It utilizes continuous analytics over existing, discoverable, and inferred metadata assets to support the design, deployment, and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms .
Data platform agnostic, data service-oriented, data governance embedded, data intelligence-driven
Flexible, reusable, and augmented data integration, abstracts complexity, supports real-time data access
Complexity in implementation, requires metadata management, may not handle all data types .
Data Lake
A centralized repository for storing data from various data sources in its original format and granularity. It supports structured, semi-structured, and unstructured data, and enables data ingestion and storage at a lower cost and higher speed than traditional data warehouses
Centralized repository, original data format, cost-effective storage, supports various data types, data ingestion
Cost-effective storage, supports various data types, enables data ingestion at scale
May lack data governance, data discovery challenges, may not handle real-time data well
Knowledge Fabric