Introduction

As real-time data becomes a core asset for modern enterprises, organizations are under growing pressure to process massive data volumes with greater speed, lower cost, and simplified architectures. Traditional streaming and analytics systems often fall short — either limited by high infrastructure cost or by the complexity of integrating multiple layers of the data stack.

To address this challenge, AutoMQ and Singdata have formed a strategic partnership to build a high-performance, cloud-native, and cost-efficient real-time data platform. By combining AutoMQ’s open-source Kafka-compatible streaming engine with Singdata’s incremental analytics capabilities, this joint solution redefines the full data lifecycle — from ingestion to insight — enabling enterprises to unlock real-time business value at scale.

About AutoMQ

AutoMQ is a fully open-source streaming data platform that is 100% compatible with the Apache Kafka protocol. It is purpose-built to solve the high cost, operational complexity, and scaling limitations of traditional Kafka in cloud environments. While maintaining full protocol compatibility, AutoMQ delivers up to 10x cost savings and 100x elasticity advantages, supporting second-level partition migration and automatic traffic rebalancing , effectively eliminating Kafka's operational pain points.

Unlike conventional Kafka implementations, AutoMQ decouples storage and compute. It replaces local disks with cloud object storage (e.g., Amazon S3, Alibaba OSS), offering inherent scalability and significant cost benefits. Enterprises can seamlessly process tens of millions of messages per second , deploy across multi-cloud environments, scale automatically, and simplify their streaming data pipelines at scale.

About Singdata

Singdata Lakehouse delivers a revolutionary cloud data platform with an advanced computing engine that achieves up to 10x performance improvements compared to traditional engines like Spark. Our cutting-edge technology enables real-time, cost-effective data processing across the entire pipeline even under massive data volumes.

As the pioneer of "General Incremental Computing," Singdata provides a unified data pipeline for seamless data integration, storage, and computation, powering the latest AI-driven innovations.

How AutoMQ and Singdata Work Together

This strategic partnership addresses widespread challenges faced by enterprises attempting to scale real-time data infrastructure—especially in Kafka + traditional data warehouse environments.

Many organizations face the limitations of traditional approaches: Kafka clusters run on high-cost configurations with frequent backlogs, traditional warehouses suffer from long processing cycles, and it's difficult to support minute-level monitoring or agile algorithm updates. As data increasingly becomes the foundation of business innovation, enterprises demand more responsive, cost-efficient, and scalable solutions.

To solve this, AutoMQandSingdata have jointly proposed a streaming data solution that spans the full pipeline—from ingestion and transmission to storage and analysis :

• At the ingestion layer , AutoMQ seamlessly replaces existing Kafka with a fully compatible, cloud-native solution that dramatically reduces messaging costs and operational burden.

• At the storage and processing layer , Singdata’s Lakehouse ingests real-time data streams via LH Pipe and uses its incremental compute engine to enable minute-level analytical updates.

This solution does not require refactoring existing Kafka-based applications and breaks away from the latency and redundancy of the traditional Lambda architecture. AutoMQ’s stateless, cloud object storage architecture combined with Singdata’s Lakehouse and incremental processing delivers a truly real-time, high-performance, and cost-efficient analytics platform .

Key Benefits for Enterprises:

• Performance Leap : Reduce data latency from next-day batch processing to near real-time, enabling faster decisions and continuous insights.

• Cost Optimization : AutoMQ cuts messaging infrastructure costs by over 50% , while Singdata improves resource efficiency to reduce overall compute and storage expenses by up to 60% .

• Simplified Architecture : A unified platform eliminates fragmented systems and reduces operational and data engineering complexity.

• Agile Business Capabilities : Enables rapid experimentation, real-time feedback loops, and faster go-to-market for recommendation engines, risk control systems, and targeted operations.

With this solution, enterprises can now process massive real-time data at significantly lower cost, respond faster to changing business demands, and gain deeper insights across critical business domains—such as real-time monitoring, intelligent operations, and personalized customer experiences.

Partnership & Future Outlook

The collaboration between AutoMQ and Singdata marks a deep integration of both parties in the fields of real-time data and Lakehouse architecture—setting a new benchmark for exceptional performance and cost optimization. Both AutoMQ and Singdata have been consistently committed to cost reduction, striving for greater economic efficiency and delivering a more streamlined, high-performance experience for users.

At the heart of this partnership, AutoMQ leverages its innovative cloud-native architecture and object storage technology to achieve significant cost savings. By using object storage as the primary data store, storage costs are dramatically reduced. The adoption of a single-replica high-availability design cuts down replication traffic, while spot instances and elastic scaling strategies on the cloud further lower compute expenses.

These measures greatly enhance the overall economic efficiency and sustainability of the system, empowering businesses to build real-time data platforms that are both high-performance and cost-effective. Learn more about AutoMQ’s cost optimization strategies and explore additional innovations.

References

[1]Singdata Lakehouse: unified incremental computing engine for real-time analytics at scale.

[2]Cost optimization strategy: cloud-native Kafka powered by AutoMQ and object storage.