The File Storage Challenges for Quantitative Trading Teams
- More and more quantitative trading teams are building their own trading, analysis and backtesting systems in public cloud or hybrid cloud. The file storage products and services need to adapt to the cloud environments;
- Machine learning and deep learning related data training practices are becoming the new norm for quantitative trading teams, posing new challenges to the performance of the file storage systems;
- The file storage also need to be friendly to the Kubernetes ecosystem because Kubernetes is becoming the de facto standard for resource orchestration.
- JuiceFS is compatible with POSIX, HDFS, S3 APIs. You can build applications using your familiar tools and frameworks and migrate legacy application code without minimal cost.
- Regardless of the size of the data, JuiceFS outperforms most of the existing cloud file systems and is more cost-effective.
- JuiceFS can be used in Kubernetes through CSI Driver, HostPath, FlexVolume, and is compatible with various Kubernetes distributions and cloud hosting services. It supports ACL configuration, subdirectory mounting, subdirectory quota, etc.
- JuiceFS protects your data safety by supporting Recycle Bin and Time Travel and protect your data privacy by supporting data transmission encryption and storage encryption.
Solutions and Benefits
- JuiceFS can be used a unified file system to support dataset storage, model training, log archiving and modeling for quantitative trading teams;
- With the JuiceFS cloud service, you can scale up or down both in terms of storage capacity and file numbers all the way up to billions of files.
- With the JuiceFS cloud service, there is no cost of migration and application transformation, no up-front investment, just pay-as-you-go.