Powering Data Architectures with Modern Technologies and Approaches

Modern data architecture is a hotbed for endless technological innovation and advancements that can best support enterprise business goals. While many organizations long for speedy, agile, scalable, and secure data architectures, they don’t know how to get started.

DBTA recently held a webinar, “Top Trends in Modern Data Architecture for 2023,” to discuss the new technologies and approaches for cultivating an excelsior data architecture that holistically addresses the myriad of modern data architecture needs.

Jamie Knowles, product manager at IDERA, offered a well-governed data supply chain as the answer to enterprises seeking effective data architectures. Employing a governance framework which unites tools, people, automation, and storefront can remediate silos between data analysts, data governance, and the data architecture.

There are a multitude of ways to accomplish this, according to Knowles: a data fabric, which joins automation and tools to work together; a data mesh, which joins the right people working together to provide the right products; and a variety of models, including traditional data models which standardize and structure data and data assets; and the data catalog, which governs data to reduce risk.  

Sam Chance, principal consultant at Cambridge Semantics , explained that applying semantics and graphs to create enterprise knowledge graphs allows anyone with an enterprise to find, understand, blend, and use enterprise data is a foremost concern for those seeking a modernized data architecture.

In a world that is far more connected than ever before, data architectures must reflect those relationships; according to Chance, nothing says connected like graphs, which require data interoperability and machine-to-machine autonomy. This places a large emphasis on joining semantics and data to create knowledge graphs, normalizing structural heterogeneity and harmonizing meaning via unambiguous ontology.

Knowledge graphs ultimately merge data mesh and data fabric technologies to combine SOA (service-oriented architecture) with data products via machine-understandable content, connecting data, domains, enterprises, communities, and ecosystems.

Tim Rottach, director of product marketing at Couchbase, emphasized that between customers and the current market, application needs have changed. They are expected to be personalized and responsive, available everywhere, real-time, dependable, developed efficiently, and deployed effectively with 100% uptime at global scale.

Similarly, database needs have changed; between demands for scalability and performance, microservice architectures, ACID transactions, and storage efficiency, it’s undoubted that modern needs for both databases and applications are signaling a change for data architecture standards.

Couchbase Capella, a distributed, SQL++ database-as-a-service for global applications that need high-performance storage, clustering, replication, and synchronization of data from the cloud, through the edge, to offline-first mobile devices, can be the solution, according to Rottach. With features like profiles and personalization, operational analytics, customer 360, product catalogs, and support for mobile and IoT applications, Couchbase Capella drives enterprise innovation at a lower cloud TCO.

Data sprawl and management issues pose significant challenges in powering a flexible and fast data architecture. Rottach explained that with Couchbase’s memory-first design, multimodel services, SQL++ query language, and incredible price for performance enables organizations to adapt to current architecture challenges with an affordable, familiar, and agile database platform.

Joseph Treadwell, head of partner and customer success at TimeXtender, continued the discussion by remarking that the modern data stack has failed; too many tools, lengthy setups, scarcity in skills and individuals, and a lacking in end-to-end orchestration compound to drastically impact the abilities of enterprise data architectures.

The solution, Treadwell explained, must be agile, holistic, and easy to use. As technology becomes increasingly difficult to access and complex to use, it renders smaller enterprises incapable of adopting robust data architectures.

TimeXtender, a robust, metadata-driven solution for data integration, allows enterprises to build data solutions 10x faster while simultaneously reducing costs by 70-80%. Incorporating over 250 sources with operational data exchange, modern data warehousing, and semantic models, TimeXtender marries documentation, data lineage, and intelligent execution for usage anywhere.

For an in-depth discussion and comprehensive examples of modern data architecture strategies, you can view an archived version of the webinar here.