Creating a Pipeline
This topic describes how to create a scheduled pipeline of jobs within a project.
As data science projects mature beyond ad hoc scripts, you might want to break them up into multiple steps. For example, a project may include one or more data acquisition, data cleansing, and finally, data analytics steps. For such projects, Cloudera Machine Learning allows you to schedule multiple jobs to run one after another in what is called a pipeline, where each job is dependent on the output of the one preceding it.
The Jobs overview presents a list of all existing jobs created for a project along with a dependency graph to display any pipelines you've created. Job dependencies do not need to be configured at the time of job creation. Pipelines can be created after the fact by modifying the jobs to establish dependencies between them. From the job overview, you can modify the settings of a job, access the history of all job runs, and view the session output for individual job runs.
Let's take an example of a project that has two jobs, Read Weblogs and Write Weblogs. Given that you must read the data before you can run analyses and write to it, the Write Weblogs job should only be triggered after the Read Weblogs job completes a successful run. To create such a two-step pipeline:
- Navigate to the project where the Read Weblogs and Write Weblogs jobs were created.
- Click Jobs.
- From the list of jobs, select Write Weblogs.
- Click the Settings tab.
- Click on the Schedule dropdown and select Dependent. Select Read Weblogs from the dropdown list of existing jobs in the project.
- Click Update Job.