In the previous article, we discussed the details of some of the architecture design of Data Substrate. In this article, we continue the discussion and elaborate on why we made these design choices and how these choices affect the resulting database solutions we built.
In this article, we dive deeper into the technical foundations of Data Substrate—highlighting the key design decisions, abstractions, and architectural choices that set it apart from both classical and modern distributed databases.
At EloqData, we've developed Data Substrate—a database architecture designed to meet the unprecedented demands of modern applications in the AI age. Unlike traditional database systems that struggle with the scale and complexity of AI workloads, Data Substrate reimagines the database as a unified, distributed computer where memory, compute, logging, and storage are fully decoupled yet globally addressable.
This series of articles explore the motivations, technical foundations, and benefits of Data Substrate, providing a comprehensive understanding of how this architecture addresses the critical challenges facing modern data infrastructure in the AI age.
Some of the topics covered are rather heavy in technical jargons, and require a good understanding of database internal mechanisms to appreciate. We apologize in advance.
This article introduces the core philosophy behind Data Substrate. We explore why traditional database architectures fall short in the AI era and present our vision for a new approach that treats the entire distributed system as a single, unified computer.
This technical deep-dive explores the architectural foundations of Data Substrate. We examine the key design decisions, abstractions, and technical choices that set Data Substrate apart from both classical and modern distributed databases.
This article examines the practical benefits and real-world implications of Data Substrate. We discuss how our design choices translate into concrete advantages for modern applications, particularly in cloud environments.
Traditional database architectures were designed for a different era—one where data volumes were smaller, workloads were more predictable, and the demands of AI applications were unimaginable. Data Substrate represents a fundamental rethinking of database design, built from the ground up for the challenges and opportunities of the AI age.
By treating the distributed system as a single, unified computer, Data Substrate eliminates many of the complexities that have traditionally made distributed databases difficult to build, operate, and reason about. This approach enables:
Ready to explore Data Substrate in action? Our open-source implementations are available on GitHub:
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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.