At its Data + AI Summit, Databricks unveiled Lakehouse//RT, an analytics engine claiming response times as low as 10 milliseconds for smaller workloads. This engine delivers real-time query performance directly to lakehouse environments, unifying operational and analytical workloads on a single data copy within a data lake. Such performance will fundamentally impact how enterprises scale AI in 2026.
Traditional data architectures typically separate operational and analytical workloads, creating latency and complexity. However, the imperative for enterprise AI demands real-time, unified access to all data sources. This separation hinders the speed and accuracy required for AI-driven decision-making and limits advanced automation.
Companies failing to adopt unified Lake Transactional/Analytical Processing (LTAP) architectures and agentic AI tools risk significant competitive disadvantage. Competitors can now make real-time, data-driven decisions and automate complex processes with sub-100ms latency, as demonstrated by Databricks' Lakehouse//RT and Omnigent.
Addressing Data Sprawl and Driving Real-World AI Impact
- Mastercard addresses data sprawl across multiple clouds by using multi-engine catalog federation to access data governed in one catalog, such as Databricks Unity Catalog, from compute engines using a different catalog like AWS Glue, according to Tech Times.
- Workday has re-examined entire business processes and deployed AI agents in production, redesigning operations around agentic systems, reports BizTech Magazine.
Mastercard's and Workday's experiences reveal the persistent challenge of data fragmentation and the transformative potential of agentic AI when data is accessible through unified platforms. Organizations must choose between maintaining complex, siloed data environments or moving towards integrated systems that enable real-time automation.
