From AI assistants and customer-facing analytics to operational intelligence platforms and real-time decisioning systems, modern data applications are creating new opportunities to drive innovation, efficiency, and competitive advantage.
Building modern data applications requires more than connecting data sources. Organizations must deliver fast, scalable experiences while ensuring data remains accurate, secure, governed, and trusted. As data volumes grow and architectures become more distributed, challenges around integration, performance, reliability, and governance continue to increase.
DBTA’s recent webinar, Building Modern Data Applications: Speed, Scale and Trust, featured Adam Luciano, VP, product management at MariaDB and Jamie Knowles, director of product at ER/Studio, who explored the technologies, architectures, and best practices behind successful modern data applications.
According to Luciano, the AI agent era has arrived. But the agents are only as good as the data underneath them. And it’s rewriting what enterprises need from a database.
MariaDB has been building for this shift all along, Luciano said. The MariaDB: Unified AI-ready Platform consists of the following features and capabilities:
- Built for modern workloads
- Scales to millions of transactions per second
- Real-time sub-ms latency
- Handles structured, vector, JSON and documents
- Broad workload support OLTP, translytical, vector, inmemory
- Modular – Single node to distributed execution
- Delivered on-prem and on Hyperscaler of choice
MariaDB also provides Vector Search, which offers semantic search, built into the database you already run.
Vector search finds records by meaning rather than exact wording. MariaDB stores those embeddings as a native data type, so semantic and transactional queries run in one engine—same SQL, same security, same backups.
When AI systems begin to reason over enterprise data, implied meaning becomes a source of systemic risk, Knowles said.
A company’s mission is to provide an enterprise tool for data architects to design and document data assets using common business models; to tightly connect data architects with data governance initiatives forming a company wide data ecosystem; and to ground AI in the meaning of data.
ER/Studio is the semantic backbone that lets architecture, governance, analytics, and AI operate from the same definitions, Knowles explained.
ER/Studio for Enterprise Data Design provides:
- Collaborative design for enterprise teams
- Reusable, business-driven models
- Governed design from concept to deployment
- AI-assisted, AI-ready data design
- Consistent modeling across platforms
“[It’s] enterprise meaning defined once. Realized consistently everywhere,” Knowles said.
For the full webinar, featuring a more in-depth discussion, Q&A, and more, you can view an archived version of the webinar here.