UniQL: A Unified Data Fabric for Heterogeneous Databases in High-Stakes Industries | IJCT Volume 12 – Issue 6 | IJCT-V12I6P65

UniQL represents a monumental leap in data fabric architecture and is specifically designed to meet the extremely demanding requirements of high-stakes industries such as finance, healthcare, energy, and government sectors. UniQL, by the use of a unified query layer that smoothly hides the difference between relational, NoSQL, cloud, and legacy systems, makes it unnecessary to move data at a high cost and risk, thus ensuring that data residency and sovereignty are maintained—very important factors in regulated environments [1, 2, 9, 20]. The AI-driven optimization and the active metadata management that are part of the same system not only allow the query performance to reach a level of excellence (as the observed latencies have been reduced by 50–80% in comparison with the traditional ETL approaches) but also enable a proactive governance, anomaly detection, and self-tuning that can adapt continually to the organization’s workloads. This smart support turns the traditionally static data fabrics into living, breathing, and flexible platforms that can be used for immediate decision-making in scenarios that are of utmost importance, for example, fraud detection in banking and patient monitoring in healthcare [2, 7, 8, 12, 4, 13]. Significantly, the UniQL query language is equipped with integrated compliance primitives that set it apart from other languages. For instance, dynamic data masking, encryption clauses, and immutable audit trails are some of the features that facilitate regulatory requirements (GDPR, HIPAA, SOX, CCPA, and Basel III) being executed right at the point of access as opposed to afterthoughts, thereby leading to a significant reduction in compliance risks and the time taken to prepare for audits [5, 13, 21]. The organization employing UniQL has a choice on how they want to implement it with the modular, containerized deployment model that is compatible with hybrid and multi-cloud strategies. Through this, they can take it on step by step, starting with read-only federated views and moving on to full governance and orchestration, without having to stop their existing operations [6, 10]. The risk of failure at the time of adoption is lowered considerably due to the presence of this gradual method and the time-to-value in enterprises that are risk-averse is sped up. Besides the above-mentioned benefits, to mention just a few, the road tests for UniQL, real and simulated world use scenarios, have borne out the following, among other, practical and measurable results: unified 360-degree views of customers or patients, reduced false positives in risk models, faster regulatory reporting, and the empowerment of business users through self-service analytics while preserving security controls [4, 13, 25]. Several promising directions are visible in the distance. One of them is the partnership of generative AI and large language models to facilitate natural-language querying that would allow non-technical stakeholders to articulate their complex analytical needs in simple English and the system would automatically translate them into optimized UniQL statements [8, 16]. An additional improvement in predictive data quality management, which involves the use of machine learning to anticipate lineage drifts or changes in schema that will impact the derivatives, would be a significant step forward in solving proactive governance issues. In addition, allowing for extension of blockchain-based immutability for audit trails in highly regulated areas is the logical next step [14]. Also, by offering a fabric overlay that respects domain boundaries while providing enterprise-wide consistency and compliance, UniQL is in line with such emerging paradigms as data mesh—a hybrid model that takes the best from centralized governance and decentralized ownership [19, 23]. The growth of data volumes is exponentially increasing, and the regulatory scrutiny is getting more and more intense, so we will have to rely on architectures that can virtualize access, automate intelligence, and embed trust. In fact, UniQL does not only allow organizations to handle data complexity but also puts them in a position to get long-term competitive advantage from it in a world that is predominantly data-driven [3, 7, 11, 22]. In brief, UniQL is a pioneer that has set a new standard for unified data fabrics: extremely efficient, highly secure, fully compliant with legal requirements, and designed to be compatible with future technological advances—the promise to deliver heterogeneous data assets into a single strategic, governed, and immediate resource for high-stakes industries in 2025 and beyond has been fulfilled [1, 2, 5, 10, ​‍​‌‍​‍‌​‍​‌‍​‍‌25].