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Data Demo. Dataplane.

Things to try out in Dataplane's demo.





On a desktop browser, login with the demo user​

Demo: https://demo.dataplane.app/webapp/login

Username: [email protected]
Password: Dataplane




Environments​

On the top right corner, click on the environments drop down to see what environments are available to you. If not already selected, change to the Development environment.

environment image




Data pipelines​

  • Be sure to be in the Development environment.
  • Select Pipelines in the menu on the left hand side.
  • Click on the Optimal Data Pipeline link.
  • Click the Run button to run the pipeline.
  • Click on the three dots in the Budget step of the pipeline and click Logs to see real-time output.
  • Click on the three dots at the top of the pipeline and click on Analytics to see the performance of the pipeline.

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Python code editor​

View, run and test the code behind the Budget step.

  • Click on the three dots in the Budget step of the pipeline and click Code.
  • In the code editor, the default python file dp-entrypoint.py will open.
  • Click on Run to run the file.

environment image




Deployments​

View and run deployments in production.

  • On the top right hand corner, click on the environment drop down menu and select Production.
  • Click on Deployments in the menu on the left hand side.
  • Click on the link to the latest deployment Optimal Data Pipeline v1.2.0.
  • Click Run. This runs an isolated version of the pipeline and avoids the development of pipelines to interfere with production runs.

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Workers​

View workers and real-time performance metrics when running analytical workloads.

  • Click on Workers in the menu on the left hand side.
  • Click on the first worker group link Python dev.

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Running in the cloud.​

This demo has the following high availability setup:

  • Running as a cluster in Digitalocean Kubernetes (2x nodes with 2GB and 1CPU)
  • Front end and networking is SSL secured and distributed at edge with Cloudflare
  • 3x Dataplane pods
  • 3x Development Python worker pods
  • 3x Production Python worker pods
  • 3x NATS message queue cluster for real-time messaging across the cluster
  • PostgreSQL with TimescaleDB extensions
  • As shown in the image, the cluster uses minimal cpu and memory resources

environment image