IBM Data Fabric – Establish an architecture to simplify data access for your users
Take advantage of the capabilities of a data fabric to address foundational data integration, governance, and compliance challenges leading to improved data usage for analytics and AI to drive business outcomes. Evolve from using point technologies to an integrated, modular, composable and re-usable insight platform to deliver business value at speed and scale.
Data is dispersed, dynamic and not the easiest to manage. By adopting a data fabric architecture, you can reduce complexity through intelligent automation.
What is a Data Fabric?
The data fabric is an emerging architecture that aims to address the data challenges arising out of a hybrid data landscape. Its fundamental idea is to strike a balance between decentralization and globalization by acting as the virtual connective tissue between data endpoints. A data fabric ensures your various kinds of data can be successfully combined, accessed, and governed both efficiently and effectively.
A data fabric is divided into four components:
Knowledge, insights and semantics
- Provides a data marketplace and shopping experience
- Automatically enriches discovered data assets with knowledge and semantics, allowing consumers to find and understand the data
Unified Governance and compliance
- Allows local management and governance of metadata but supports a global unified view and policy enforcement
- Automatically applies policies on data assets following global and local rules
- Utilizes advanced capabilities to automate data asset classification and curation
- Automatically establishes queryable access routes for any catalogued assets for increased activation of data
Intelligent integration
- Accelerates a data engineer’s tasks through automated flow and pipeline creation across distributed data sources
- Enables self-service ingestion and data access over any data with local and global deep enforcement of data protection policies
- Automatically determines best fit execution through optimized workload distribution and self-tuning and correction of schema drifts
Orchestration and lifecycle
- Enables the composition, testing, operation and monitoring of data pipelines
- Infuses AI capabilities in the data lifecycle to automate tasks, self-tune, self-heal and detect source data changes, all of which facilitate automated updates
Business Benefits of a Data Fabric
When implemented correctly, a data fabric helps ensure those values are available throughout the organization in the most efficient and automated way possible.
As such, the fabric has three key benefits:
Enable self-service data consumption and collaboration.
- Business users have a single point of access to find, understand, shape and consume data throughout the organization.
- Centralized data governance and lineage help users understand what the data means, where it comes from, and how it is related to other assets.
- Extensive and customizable metadata management scales easily and is accessible via APIs.
- Self-service access to trusted and governed data enables line-of-business collaboration with other users.
Automate governance, protection and security; enabled by active metadata.
- Agility, security, and productivity are increased for data engineers, data scientists, and business analysts.
- Multiple global data sources appear as one database.
- New, industry-leading discovery of personally identifiable information (PII) and critical data elements is possible at a massive scale.
Automate data engineering tasks and augment data integration across hybrid cloud resources.
- Automatically optimized data integration helps accelerate data delivery.
- Automatic workload balancing, and elastic scaling means jobs are ready for any environment and any data volume.
- Resiliency and CI/CD automation are built-in.
- The automated process for capturing changes in real-time supports the delivery of quality data for business processes.
- Machine learning can automate and extend custom data discovery, classification and curation processes, leading to faster time-to-value.
- Continuous analysis can be automatically performed in real-time, wherever data lives.
Contact our expert team to assist you in the integration and engagement of all your organization’s data for better business outcomes.
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