Eliminate Data Silos
with Data Fabric

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 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 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.

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.

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.

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

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 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.

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.

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.

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.

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.

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.