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Eliminate Data Silos
with Data Fabric

Amid the surge in big data driven by AI, hybrid clouds, and IoT, a data architecture that can address key data challenges such as integration, governance, and analytics is the need of the hour. Discover how data fabric, a contemporary data architecture, can solve these problems and turn your organization into a data-driven enterprise.

Eliminate Data Silos with Data Fabric

Why Data Fabric?

Data fabric architecture facilitates seamless integration, access, and management of data across diverse and distributed data sources. It aims to create a unified and agile data infrastructure that enables organizations to efficiently store, retrieve, and analyze data.

Data fabric solutions typically provide data virtualization, data orchestration, and data governance capabilities to help organizations harness the value of their data assets.

By implementing data fabric in your organization you can:

  • Automate various data management tasks
  • Facilitate data discovery
  • Foster data collaboration across the organization
  • Enable real-time analytics
  • Support multi-cloud environments

Data Hub Vs Data Mesh Vs Data Fabric

Data Hub

Data hub is typically designed to centralize and manage data from various sources in a single repository. It acts as a centralized data store and can be used for multiple purposes, including integration.. , storage, and processing.Data hubs are commonly used for business intelligence, reporting, and data warehousing. It is also used as an intermediary storage point for data that is later moved to other systems for further processing.
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Data Mesh

Data mesh is designed to handle the complexities of large-scale data systems while allowing organizations to leverage data more effectively. It places a strong emphasis on decentralization.. , autonomy, and productization.Data Mesh is best suited for organizations dealing with vast amounts of data and multiple domains or product lines. It is helpful for organizations seeking to implement data-driven decision-making and analytics at scale.
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Data Fabric

Data fabric is designed to provide a more dynamic, real-time, and agile approach to data management. It is focused on creating a connected data ecosystem where data is seamlessly.. accessible, discoverable, and usable by different applications and services.Data fabric is suitable for complex and dynamic environments where data needs to be accessible in real-time for various purposes, such as analytics, AI/ML, and IoT.
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Data Hub

Data hub is typically designed to centralize and manage data from various sources in a single repository. It acts as a centralized data store and can be used for multiple purposes, including integration, storage, and processing.

Data hubs are commonly used for business intelligence, reporting, and data warehousing. It is also used as an intermediary storage point for data that is later moved to other systems for further processing.

Data Mesh

Data mesh is designed to handle the complexities of large-scale data systems while allowing organizations to leverage data more effectively. It places a strong emphasis on decentralization, autonomy, and productization.

Data Mesh is best suited for organizations dealing with vast amounts of data and multiple domains or product lines. It is helpful for organizations seeking to implement data-driven decision-making and analytics at scale.

Data Fabric

Data fabric is designed to provide a more dynamic, real-time, and agile approach to data management. It is focused on creating a connected data ecosystem where data is seamlessly accessible, discoverable, and usable by different applications and services.

Data fabric is suitable for complex and dynamic environments where data needs to be accessible in real-time for various purposes, such as analytics, AI/ML, and IoT.

Organizations may use a combination of these architectures to create a comprehensive and agile data ecosystem that meets their specific data-related needs. To decide between these approaches, organizations should assess their organizational structure, cultural readiness, technical maturity, security needs, and resource availability.

Data Fabric Workflow

Data Fabric Workflow

Data Fabric Use Cases

While other architectures may also support some of these use cases, data fabric’s ability to abstract technical complexities, real-time access, and easy data integration uniquely suits these scenarios.

Data Governance and Compliance

Data Governance and
Compliance

Data fabric includes features for data governance, data quality management, and data lineage tracking, which are essential for ensuring compliance with regulations. It provides transparency into how data is used and governed.

Multi-Cloud and Hybrid Cloud Environments

Multi-Cloud and Hybrid
Cloud Environments

Data fabric is particularly useful in multi-cloud and hybrid cloud environments where data must flow seamlessly between cloud providers or on-premises and cloud environments.

Real-Time Analytics

Real-Time
Analytics

Data fabric providing real-time data access and simplifying integration complexities makes it an excellent choice for handling real-time analytics tasks.

Data Integration and ETL

Data Integration
and ETL

Data fabric simplifies data integration and extract, transform, and load (ETL) processes by providing a unified and interconnected data environment. This makes it easier to combine data from diverse sources for analytics and reporting.

Data Migration and Replication

Data Migration and
Replication

Data fabric's ability to manage data across different environments, such as on-premises systems and the cloud, makes it valuable for data migration and replication use cases. It ensures data interoperability and portability.

Data Governance and Compliance

Data fabric includes features for data governance, data quality management, and data lineage tracking, which are essential for ensuring compliance with regulations. It provides transparency into how data is used and governed.

Multi-Cloud and Hybrid Cloud Environments

Data fabric is particularly useful in multi-cloud and hybrid cloud environments where data must flow seamlessly between cloud providers or on-premises and cloud environments.

Real-Time Analytics

Data fabric providing real-time data access and simplifying integration complexities makes it an excellent choice for handling real-time analytics tasks.

Data Integration and ETL

Data fabric simplifies data integration and extract, transform, and load (ETL) processes by providing a unified and interconnected data environment. This makes it easier to combine data from diverse sources for analytics and reporting.

Data Migration and Replication

Data fabric's ability to manage data across different environments, such as on-premises systems and the cloud, makes it valuable for data migration and replication use cases. It ensures data interoperability and portability.

Data Fabric Implementation

Implementing a data fabric in your organization involves a strategic approach that aligns with the organization’s overall data strategy. Here are the steps we consider when implementing a data fabric:

Data Fabric Implementation
Define Data Objectives and Sources

We initiate the implementation process by understanding your organization's data objectives and identifying the specific business challenges you aim to address. We collaborate with subject matter experts to ensure the objectives are refined and locate the necessary data sources to streamline integration efforts.

Develop a Robust Data Framework

Our data experts will create a comprehensive data framework that aligns closely with your defined data objectives. For this, we determine the key entities and data elements essential to support your data-driven initiatives. Clear boundaries and limitations are established for the scope of the data framework. We can leverage existing data models or catalogs for consistency.

Integrate Data Seamlessly

We ensure a seamless connection between your data framework and the data sources is established. Our expertise in data virtualization ensures streamlined access to data without the need for cumbersome extraction and loading processes. In scenarios where virtualization may not be suitable, we provide solutions that combine virtualization and materialization capabilities.

Enable Data Accessibility

Once data is integrated and readily available, we will ensure users and applications can access it. For this, we provide various options including integrating the data framework into existing applications, utilizing business intelligence tools, or employing visualization tools for effective data exploration and analysis, etc.

Adapt and Expand

Our service embraces an iterative approach to adapt and expand your data fabric framework continually. This flexibility allows us to revisit and refine your data framework in response to new questions or changing data requirements, maximizing the value of your data initiatives.

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