Orchestrate technologies and operationalize projects through DevOps implementation
Saagie DataOps Platform not only integrates best-in-class commercial and open-source technologies via Kubernetes and Docker, it offers a production-grade workspace to automate and monitor the entire data life cycle and help teams collaborate throughout projects.
Orchestrate technologies and run pipelines
Choose your technologies
Combine ready-to-use frameworks and their various versions from the open-source, commercial and cloud-native worlds.
Make your jobs reproducible
Meet production criteria with our containerization capabilities and run processing jobs in exploration mode as well as in production.
Run your jobs in pipelines
Build workflows to run ETL, pre-processing and processing jobs and manage data lifecycles through versioning and logs.
Implement DevOps practices
Apply continuous deployment
Use our APIs and plugins to connect your IDE and continuous integration tools and automate the continuous deployment chain.
Run versioning
Document your job’s versions, monitor logs for each running instance and roll back whenever you need.
Switch environments
Automate the promotion of your data processing jobs between environments (Lab, Development, QA, Staging, Production) with the Gradle DataOps plugin.
Operationalize data projects
Isolate projects
Create a project for each use case with dedicated teams and technologies, link it to your AD/LDAP groups and automate network security isolation.
Increase traceability
Centralize and store logs for easy sharing and debugging between your Data and Ops teams, configure your Kubernetes to route them to your favorite log management system (ELK, Splunk, CloudWatch) and keep an audit.
Schedule and monitor processes
Operations of data tooling are harmonized to have an interoperable orchestration. Schedule jobs inside Saagie or through an external scheduler ($U, Control-M, WebSphere/Tivoli Workload Scheduler), monitor their status (via UI or API) and get an activity overview to improve your production processes.
