Cloud Dataflow Jobs and Templates

A job is an executing pipeline in Cloud Dataflow. There are two ways to execute jobs:

1. traditional method,

2. template method.

With the traditional method, developers create a pipeline in a development environment and run job from that environment. The template method separates development from staging and execution.
With the template method, developers still create pipelines in a development environment, but they also create a template, which is a configured job specification. The specification can have parameters that are specified when a user runs the template. Google provides a number of templates, and you can create your own as well.
 
After selecting a template, you can specify parameters, such as source and sink specifications.
Jobs can be run from the command line and through the use of APIs as well. For example, you could use the 'gcloud dataflow jobs run command' to start job. An example of a complete 'job run' command looks like this:

gcloud dataflow jobs run pde-job-1 \
--gcs-location gs://pde-exam-cert/templates/word-count-template

This command creates a job named pde-job-1 using a template file called 'word-count template' located in the 'pde-exam-cert/templates' bucket.

Using the Google Cloud Dataflow Runner

The Google Cloud Dataflow Runner uses the Cloud Dataflow managed service. When you run your pipeline with the Cloud Dataflow service, the runner uploads your executable code and dependencies to a Google Cloud Storage bucket and creates a Cloud Dataflow job, which executes your pipeline on managed resources in GCP.

The Cloud Dataflow Runner and service are suitable for large scale, continuous jobs, and provide:

1. A fully managed service,
2. Autoscaling of the number of workers throughout the lifetime of the job.
3. Dynamic work rebalancing.
The Beam Capability Matrix documents supported capabilities of the Cloud Dataflow Runner.

Cloud Dataflow Runner prerequisites and setup

To use the Cloud Dataflow Runner, you must complete the setup in the 'Before you begin' section of the 'Cloud Dataflow quickstart' for your chosen language:

1. Select or create a GCC project.
2. Enable billing for your project.
3. Enable the required Google Cloud APIs: Cloud Dataflow, Compute Engine, Stackdriver Logging, Cloud Storage, Cloud Storage JSON, and Cloud Resource Manager. You may need to enable additional APIs (such as BigQuery, Cloud Pub/Sub, or Cloud Datastore) if you use them in your pipeline code.
4. Athenticate with GCP.
5. Install the Google Cloud SDK.
6. Create a Cloud Storage bucket.

Specify your dependency:
When using Java, you must specify your dependence on the Cloud Dataflow Runner in your 'pom.xml'

Self executing JAR

In some cases, such as starting a pipeline using a scheduler such as Apache AirFlow, you must have a self-contained application. You can pack a self-executing JAR by explicitly adding the following dependency on the Project section of your pom.xml, in addition to the adding existing dependency.



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