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.
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View All TagsA Deeper Dive Into Data Substrate Architecture
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.
Data Substrate Technology Explained
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.
1. Data Substrate: Motivation and Philosophy
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.
2. A Deeper Dive Into Data Substrate Architecture
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.
3. The Benefits of Data Substrate Architecture
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.
Why Data Substrate Matters
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:
- Modular architecture enables community collaboration and avoid reinventing the (many) wheels
- True scalability without sacrificing consistency
- Independent resource scaling for compute, memory, logging, and storage
- Better performance through optimized hardware utilization and innovative algorithm design
- Cloud-native features like auto-scaling and scale-to-zero
- Simplified development through familiar single-node programming models
Get Started with Data Substrate
Ready to explore Data Substrate in action? Our open-source implementations are available on GitHub:
- EloqKV: A high-performance key-value store built on Data Substrate
- EloqSQL: A MySQL-compatible distributed SQL database
- EloqDoc: A document database for modern applications
Join our Discord community to connect with other developers and stay updated on the latest developments in Data Substrate technology.
The Rise of Object Storage in Cloud OLTP Architecture
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.
Exploring EloqKV Decoupled Architecture: A Tour with an Agentic AI Case Study
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.
Building a Data Foundation for Agentic AI Applications
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.
Why We Develop EloqDB Mainly in C++
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++.
ACID in EloqKV : Atomic Operations
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.
Introduction to Data Substrate
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.