At the recent Data Stream Summit 2025, Hubert Zhang, CTO of EloqData, delivered a talk on building elastic, agentic AI data pipelines using Apache Pulsar and EloqDoc.
In the previous blog, we discussed the future database foundation for Agentic AI Applications. In this blog we will simplify the agentic application and use EloqKV as data store to explore EloqKV's decoupled architecture.
We have recently open sourced our three products: EloqKV, EloqSQL, and EloqDoc. These offerings reflect our commitment to addressing the evolving demands of modern data infrastructure, particularly as we enter an era dominated by powerful, autonomous AI systems.
LLM-powered Artificial Intelligence (AI) applications are driving transformative changes across industries, from healthcare to finance and beyond. We are rapidly entering the Agentic Application Age, an era where autonomous, AI-driven agents not only assist but actively make decisions, manage tasks, and optimize outcomes independently.
However, the backbone of these applications—the data infrastructure—faces immense challenges in scalability, consistency, and performance. In this post, we explore the critical limitations of current solutions and introduce EloqData’s innovative approach specifically designed to address these challenges. We also share our vision for an AI-native database, purpose-built to empower the Agentic Application Age, paving the way for smarter, more autonomous, and responsive AI applications in the future.
We have recently introduced EloqKV, our distributed database product built on a cutting-edge architecture known as Data Substrate. Over the past several years, the EloqData team has worked tirelessly to develop this software, ensuring it meets the highest standards of performance and scalability. One key detail we’d like to share is that the majority of EloqKV’s codebase was written in C++.
In the previous blog, we discussed the durable feature of EloqKV and benchmarked the write performance of EloqKV with the Write-Ahead-Log enabled. In this blog, we will continue to explore the transaction capabilities of EloqKV and benchmark the performance of distributed atomic operations using the Redis MULTI EXEC commands.
In this blog post, we introduce our transformative concept Data Substrate. Data Substrate abstracts core functionality in online transactional databases (OLTP) by providing a unified layer for CRUD operations. A database built on this unified layer is modular: a database module is optional, can be replaced and can scale up/out independently of other modules.