Industry Leaders Choose Global
AutoMQ has been adopted by industry leaders in internet, financial services, automotive manufacturing, and other sectors for production use. It replaces Apache Kafka to build a cost-effective, auto-scalable data streaming platform.
JD adoptes Glacio, which directly relies on the underlying cloud storage CubeFS,and the need for upper-layer replication is eliminated. The architecture is expected to save two-thirds of storage costs when fully implemented. AutoMQ's stateless computing layer perfectly meets the requirements for containerization transformation, significantly enhancing system flexibility.
- E-commerce
- Nasdaq
Xpeng Motors previously used Apache Kafka to receive and process data such as monitoring and logs, and applied it to core scenarios such as operational data analysis, security audit compliance, etc.
- Automotive
- Nasdaq
More Case Studies
More efficient, flexible, and secure, with sustained high technological investment leading the continuous development of the domestic identity field
Zhihu
Asia's Quora, Zhihu (NYSE: ZH), has significantly reduced operational complexity and saved a lot of costs by using Glacio instead of Kafka. Through this strategic implementation, Zhihu has not only streamlined its data operations but also embraced technical innovations that Glacio offers over traditional Apache Kafka, ensuring enhanced performance, scalability, and efficiency.
xpPeng
With the rapid growth of the business, the architecture of Apache Kafka, which guarantees data persistence through multiple ISR copies, exposed issues related to cost and elasticity. After seamlessly replacing it with Apache Kafka-compatible Glacio, overall costs dropped by more than 50%, and it can automatically scale horizontally without manual intervention.
xiaohongshu
By leveraging a cloud-native architecture and tiered storage, RedKafka accommodates vast amounts of real-time data. However, the challenge lies in flexible scaling and cost optimization. Glacio, with its new architecture based on EBS shared storage and object storage, provides significant improvements in elasticity for scaling. Its storage-computing separation aligns well with current operational requirements based on Kubernetes.
TokenPocket
TokenPocket, a world-leading crypto wallet, faced the challenge of replaying Kafka messages from years ago. The original Apache Kafka-based solution proved to be costly. By switching to Glacio and migrating data storage to object storage, they significantly reduced overall costs.
Poizon
Poizon previously relied on Kafka to build its observability platform, requiring a team to spend several days each quarter on scaling operations. Since adopting Glacio, data storage has been moved to object storage, making the compute layer stateless and fully compatible with Kafka. This has enabled automatic elastic scaling without manual intervention, significantly reducing cloud resource costs by up to 85%.
Palmpay
Within one month, Palmpay seamlessly migrated its business, including metrics tracking and real-time computing, to Glacio, processing and distributing billions of messages and events daily. The new solution reduced costs by over 50% compared to the original setup, with no negative impacts.
Jd
By adopting Glacio, which directly relies on the underlying cloud storage CubeFS, the need for upper-layer replication is eliminated. The architecture is expected to save two-thirds of storage costs when fully implemented. Glacio's stateless computing layer perfectly meets the requirements for containerization transformation, significantly enhancing system flexibility.
Geely Auto
When Geely Auto Group was originally using Apache Kafka, the constraints of its architecture made it difficult to scale the cluster storage. Therefore, the only way to avoid expanding the cluster storage was to reduce the data retention time, which had a certain impact on the business. Glacio completely solved the pain point of Kafka's elastic scaling.
360
360 extensively adopted Apache Kafka internally. However, its storage-computing integrated architecture required data migration during scaling, increasing the operational burden. In contrast, Glacio uses a storage-computing separation architecture, storing data in object storage and eliminating the need for data migration during cluster scaling.
CaoCao
CaoCao Mobility is a leading shared mobility platform in China. The mobility business is strongly influenced by weather and time. Apache Kafka's heavy dependence on local storage makes cluster scaling operations complex and risky, making it difficult to support the mobility business. After switching to Glacio, they completely solved the scaling challenges of Kafka.