Wave containers
Next generation container provisioning for data analysis
- Simplify pipeline development and deployment
- Improve pipeline performance with platform-optimized containers
- Authenticate against container registries with centralized credential management
- Improve productivity by focusing on pipeline logic
- Augment containers in secure or regulated environments
- Maximize portability using Conda packages across environments
Develop faster. Deliver efficiently
Rethinking containers for cloud-native data pipelines
Containers are an essential part of data analysis in the cloud. Building and delivering optimized, context-aware container images slows down development. Wave is a container provisioning service designed for use with data analysis applications such as Nextflow. It allows for the on-demand assembly, augmentation, and deployment of containerized images based on task requirements.
Features
Conda based containers
Wave simplifies Conda-based container deployment in cloud environments. It dynamically generates container images from Conda recipes, assembling required packages declared in your Nextflow pipeline.
Extend Existing Containers
Wave extends existing containers without rebuilding, accommodating regulatory and security requirements. Developers can add custom scripts and resources to meet specific needs.
Private Registry Access
Integrating with Tower Cloud credentials for seamless access to and publishing of containers in private registries, simplifying authentication against cloud-specific registries.
Multi-Cloud Container Deployment
To optimize multi-cloud pipelines, Wave mirrors containers across accounts, regions, and providers on-demand during pipeline execution, enhancing performance and cost-efficiency.
Architecture-Optimized Workloads
Wave provision containers on-demand, catering to specific hardware architectures like amd64 and arm64, depending on the execution platform.
Near caching
For scalable production pipelines, Wave enables transparent caching of container images in the same region as computation, reducing data transfer costs and time during deployment