Data fabric is defined as an architecture and a set of data services that provides continuous capabilities across a choice of endpoints spanning from hybrid to a multi-cloud environment. Users have access to the respective data via the data fabric in a governed way to share with anyone, regardless of the person’s location. Data Fabric is an architectural approach that is applicable to a set of technologies that allow you to break down the data and ensure it reaches the user. It enables accessing, absorbing, integrating and sharing data across organisations, whether on-premises or in multiple-cloud environments.

The 3 responsibilities of Data Fabric

1. Accessing Data
2. Managing the Lifecycle of the data
3. Exposing data

1. Accessing Data

Some usual places for a business to access data would be the Enterprise Data Warehouse (EDW), Data Lakes, and Relational Database Systems (RDMS). There is a high possibility for data to be found on SaaS applications due to the extensive use of SaaS products to keep track of the critical customer information gathered.

There is a need to collect all the data from all these sources without copying or moving the data. To do so, Data Fabric allows leveraging something called the Virtualization layer to accumulate all the data from these sources without moving or copying them to a different repository.

Nevertheless, not all data or documents can be accessed similarly. Some data must be copied or duplicated for access and need data pipelines to move data. The Data Fabric must have robust data integration tools (ETL) to do so. This helps move the data from one place to the central repository without hassles.

2. Managing the Lifecycle of the data

This is the responsibility of the Data Fabric with two perspectives. One would be the governance and privacy policies, and the other would be an organisation’s compliance. Data Fabric also plays a huge role in these two aspects, as the purpose of following rigid governance and privacy policies is to ensure that the right individuals in an organisation have access to the right data and nothing more. The policies are enforced by Active metadata automation. Active metadata refers to the automatically generated and updated data by a system or application.

3. Exposing Data

Exposing data can be referred to as ensuring that the collected data is made available to the right set of users through multiple enterprise search catalogues. The users of the data could be business analysts, scientists, app developers and so on. Users further analyse the acquired data using various tools such as Business Intelligence or Predictive Analytics and Machine Learning platforms or PAML. Data Fabric should support all of these functionalities and open-source technologies such as Python or Spark and many more such technologies.

When Data Fabric supports all the above functionalities, it helps business analysis analyse the data better and helps app developers build customisable applications.

4. Trustworthy AI

This part of the Data Fabric involves robust MLOps tools to operationalise machine learning projects. These tools also help monitor our results’ bias, fairness and explainability.

For instance: Starting a hotel.

Firstly, the required data is sourced from various platforms and resources such as social media pages, customer data, and credit card purchase lists to check their buying habits. This also requires a Master Data Management tool or MDM tool to help recheck the customers’ data that were acquired for its accuracy.

The governing policies are then applied to the collected data, for example, masking sensitive information and redacting personal information acquired for the credit card behavioural analysis. Then the data is published in catalogues through enterprise search catalogues. Custom-building application developers then buy this. The search engine recommends that. This gives an insight into the custom habits and behaviour of the end user.

What are the policies?

Some of the policies are:

1. Masking certain aspects of data.
2. Redacting sensitive information that might be found in the data.
3. Role-based access control method (RBAC) refers to the specific role in which an individual is placed in an organisation and the privileged data access control shared with them.

Data Fabric provides organisations with rich lineage information that details the source of the data, the transactions of the data, and from where it has been acquired. This helps to access the data for its quality.

Compliance

Data Fabrics help in data regulations such as CCPA, GDPR, HIPAA for the healthcare industry and FCRA for financial services. Data Fabric helps define compliance policies such as these and many others.

Conclusion

Data Fabric is used to build a personalised, high-quality user experience. These measures taken by the Data Fabric prove to be a shielding coat over cybersecurity by safeguarding sensitive data from being breached, falling into the wrong hands, preventing identity theft, and data leakage, to uphold confidentiality and the integrity of the users data.