Creating a Datafabric Instead Of Data Silos

A snapshot of the challenges of an evolving Data Landscape??

• Skill shortage?
• Data inconsistencies?
• Duplicate data?
• Data latency?
• Data security concerns?
• Data silos?
• Moving data to the cloud?
• Data wrangling?

To tackle these, enterprises are collecting data from a variety of platforms at an accelerating pace. Using tools like?

• Enterprise Data Warehouse/Data cloud: Large central repositories for organizing business operational data. In the past, they were hosted locally in on-premises systems, but have recently shifted to more cloud-native managed services.?
• Data Lake: Centralized repository for storing all structured and unstructured data at any scale?
• Data Lakehouse: A combination of the first two, combines a data lake's flexibility in data types and scalability with some of the more structured and high-quality data components of a data warehouse.?

These tools are typically used for analytics and operational reporting, but they traditionally necessitate manual data duplication and relocating into their central repositories. A data fabric architecture is a classic use case since it provides connective tissue across data endpoints for improved integration, discovery, governance, filtering, and orchestration.?

What is a Data fabric?

It is an architectural approach and set of technologies that allows data silos to be broken down and data into the hands of data users. It enables governed access, ingest, integration, and sharing of data across an enterprise, regardless of location, whether in on-premises systems or multiple public cloud environments.?

Major functionalities of data fabric:?

1. Data from EDW, data lakes, relational database systems, and SaaS applications containing critical insights are accumulated. A data fabric enables the use of a virtualization layer to aggregate access to these data sources and initiates using them without moving or copying them to another repository.?
2. Contains robust data integration tools or ETL tools for latency requirements and formal data pipelines.?

Managing the lifecycle of the data:?

1. Governance and privacy: A data fabric employs active metadata to automate the enforcement of enterprise policies. This allows for the masking of certain aspects of data sets, the redaction of some confidential details from the data, and the definition of access based on a role-based access control method. Data fabric also provides data lineage information, i.e., where the data came from, what transformations were performed on it, and evaluates the data for quality.?
2. Compliance: A data fabric aids in the definition of compliance policies and various data regulations around the world. GDPR, CGPA, HIPAA (for healthcare), and FCRA are a few examples (for financial services).?

Exposing data to users?

Occurs after connecting all data requirements, defining all governance policies, and applying them to data sets via multiple enterprise search catalogues.?

• Data flows into catalogues and is made available to users such as Business Analysts, Data Scientists, and Application Developers. They also employ various tools such as BI or predictive analytics, as well as machine learning platforms. This implies that a data fabric should support multiple vendors for these platforms, as well as open source technologies such as Python, Spark, and others. This is accomplished by exposing data from the catalogue via various API endpoints.?
• Trustworthy AI (Artificial Intelligence): This includes employing robust MLOps tools to operationalize our ML projects, as well as tools to aid in the monitoring of bias, fairness, and predictability in the results.?

Characteristics of a data fabric:?

• Data democratization: Automating key components of a company's data pipeline (such as integration, security, transformation, governance, preparation, orchestration, and curation).?
• Data integration: Integrating all data from various data sources into a single, unified view to reduce data complexity for users and enterprises, allowing faster and more efficient data access?
• Data Quality Management: Data set quality is auto-curated during data ingestion.?
• Real-time data governance: Manages granular data-access controls; mitigates security risks with row/column level data masking and encryption; keeps a history for each workflow?
• Data Sharing: This allows enterprises to set up a classified data exchange hub where data providers can share live data with data consumers and set data access permissions.?

The future with data fabric??

By 2025, enterprises using data fabrics will be able to dynamically connect, optimise, and automate data management activities, based on the scope of the possibilities discussed in this article. With this, a future in which data fabric platforms make data management more dynamic in hybrid and multi-cloud environments can be envisioned.?