epam/cloud-pipeline

Cloud agnostic genomics analysis, scientific computation and storage platform

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epam/cloud-pipeline has 150+ stars and 60+ forks on GitHub, is written in Java, and was last pushed on Oct 18, 2026. Data as of Oct 18, 17:50 UTC. Signed-in members get hourly updates — create a free account.

Summary Information

Updated 21 minutes ago

Type:Server / PlatformCategory(s):Bioinformatics & Life Sciences Data Pipelines & ETL/ELT Platform Engineering & PaaS Science & Industry

Added to GitGenius on October 12th, 2026

Created on March 6th, 2019

Open Issues & Pull Requests: 1,138 (+0)

GitHub issues: Enabled

Number of forks: 66

Total Stargazers: 163 (+0)

Total Subscribers: 20 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 0.0 hours

Mean response time: 6.5 days

90th percentile: 13.8 days

Tracked items: 238

Most active contributors

Related repositories by overlapping contributors

grafana/alloy

Maintainer activity

11 people did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 18, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits. How this is counted.

How this project is maintained

Roughly one issue in four opened in the past year never receives a reply. 48% of open issues come from outside the core team, a mix of external reports and the maintainers' own roadmap. 49% of tracked open issues have had no activity in three months. Only 48% of issues opened in the past year have been closed. Three people close 92% of everything that gets resolved.

Issue Activity (beta)

Open issues: 214

New in 7 days: 16

Closed in 7 days: 0

Avg open age: 392 days

Stale 30+ days: 163

Stale 90+ days: 148

Recent activity

Opened in 7 days: 16

Closed in 7 days: 0

Comments in 7 days: 4

Events in 7 days: 94

Top labels

kind/enhancement (219)
kind/bug (110)
intel/artificial 🤖 (40)
state/has-doc (22)
state/underway (22)
sys/core (21)
state/has-case (18)
sys/gui (15)

Most active issues this week

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Detailed Description

Cloud Pipeline is a cloud-agnostic platform for genomics analysis, scientific computation, and data storage that unifies AWS, GCP, and Azure resources into a single service.

The platform addresses the complexity of running scientific workloads across multiple cloud providers by abstracting away provider-specific details. It works by wrapping compute and storage resources from AWS, Google Cloud Platform, and Microsoft Azure into a unified interface. Users define data processing pipelines as versioned workflow scripts, manage data storage with file version control through the web interface, deploy custom calculation environments using Docker containers, and launch GUI-based scientific applications that expose web endpoints or remote desktop connections. When a pipeline runs, Cloud Pipeline provisions cloud instances and executes the specified Docker image on them.

Cloud Pipeline suits teams conducting genomics analysis, chemistry modeling, or other scientific computation who need to work across multiple cloud providers without rewriting infrastructure code for each one. It is particularly valuable for organizations that want to avoid vendor lock-in or need flexibility in choosing cloud resources based on cost or availability. The platform provides both a web-based GUI and CLI access to most features, making it accessible to users with different technical preferences. Anyone adopting it should expect to define pipelines as scripts, manage Docker images for their computational environments, and configure cloud instance types through the interface.

The project maintains active development with regular updates to its codebase and documentation. The repository includes comprehensive documentation sources alongside prebuilt binaries available through releases, indicating sustained effort to support users. Development activity spans multiple areas including core platform features, cloud provider integrations, and tooling support, suggesting a team working across different functional domains rather than focusing narrowly on a single aspect.