update readme

vincentclaes · Jan 29, 2021 · b0d56ed · b0d56ed
1 parent 510469e
commit b0d56ed
Showing 1 changed file with 48 additions and 47 deletions.
diff --git a/README.md b/README.md
@@ -26,52 +26,51 @@ Ideas to be implemented can be found [below](#ideas)
     pip install datajob
     npm install -g aws-cdk
 
-# Example
-
-See the full code of the example [here](https://github.com/vincentclaes/datajob/tree/main/examples/data_pipeline_simple)
+# Quickstart
 
+## Configure the pipeline
 **We have 3 scripts that we want to orchestrate sequentially and in parallel on AWS using Glue and Step Functions**.
 
+    from datajob.datajob_stack import DataJobStack
+    from datajob.glue.glue_job import GlueJob
+    from datajob.stepfunctions.stepfunctions_workflow import StepfunctionsWorkflow
+
+
+    # the datajob_stack is the instance that will result in a cloudformation stack.
+    # we inject the datajob_stack object through all the resources that we want to add.
+    with DataJobStack(stack_name="data-pipeline-simple") as datajob_stack:
+
+        # here we define 3 glue jobs with the datajob_stack object,
+        # a name and the relative path to the source code.
+        task1 = GlueJob(
+            datajob_stack=datajob_stack,
+            name="task1",
+            job_path="data_pipeline_simple/task1.py",
+        )
+        task2 = GlueJob(
+            datajob_stack=datajob_stack,
+            name="task2",
+            job_path="data_pipeline_simple/task2.py",
+        )
+        task3 = GlueJob(
+            datajob_stack=datajob_stack,
+            name="task3",
+            job_path="data_pipeline_simple/task3.py",
+        )
+
+        # we instantiate a step functions workflow and add the sources
+        # we want to orchestrate. We got the orchestration idea from
+        # airflow where we use a list to run tasks in parallel
+        # and we use bit operator '>>' to chain the tasks in our workflow.
+        with StepfunctionsWorkflow(
+            datajob_stack=datajob_stack, name="data-pipeline-simple"
+        ) as sfn:
+            [task1, task2] >> task3
+
 The definition of our pipeline can be found in `examples/data_pipeline_simple/datajob_stack.py`, and here below:
 
-from datajob.datajob_stack import DataJobStack
-from datajob.glue.glue_job import GlueJob
-from datajob.stepfunctions.stepfunctions_workflow import StepfunctionsWorkflow
-
-
-# the datajob_stack is the instance that will result in a cloudformation stack.
-# we inject the datajob_stack object through all the resources that we want to add.
-with DataJobStack(stack_name="data-pipeline-simple") as datajob_stack:
-
-    # here we define 3 glue jobs with the datajob_stack object,
-    # a name and the relative path to the source code.
-    task1 = GlueJob(
-        datajob_stack=datajob_stack,
-        name="task1",
-        job_path="data_pipeline_simple/task1.py",
-    )
-    task2 = GlueJob(
-        datajob_stack=datajob_stack,
-        name="task2",
-        job_path="data_pipeline_simple/task2.py",
-    )
-    task3 = GlueJob(
-        datajob_stack=datajob_stack,
-        name="task3",
-        job_path="data_pipeline_simple/task3.py",
-    )
-
-    # we instantiate a step functions workflow and add the sources
-    # we want to orchestrate. We got the orchestration idea from
-    # airflow where we use a list to run tasks in parallel
-    # and we use bit operator '>>' to chain the tasks in our workflow.
-    with StepfunctionsWorkflow(
-        datajob_stack=datajob_stack, name="data-pipeline-simple"
-    ) as sfn:
-        [task1, task2] >> task3
-
-
-## Deploy, Run and Destroy
+
+## Deploy
 
 Set the aws account number and the profile that contains your aws credentials (`~/.aws/credentials`) as environment variables:
 
@@ -83,14 +82,19 @@ Point to the configuration of the pipeline using `--config` and deploy
     cd examples/data_pipeline_simple
     datajob deploy --config datajob_stack.py
 
+# Run
 After running the `deploy` command, the code of the 3 tasks are deployed to a glue job and the glue jobs are orchestrated using step functions.
 Go to the AWS console to the step functions service, look for `data-pipeline-simple` and click on "Start execution"
 
 ![DataPipelineSimple](assets/data-pipeline-simple.png)
 
-Follow up on the progress. Once the pipeline is finished you can pull down the pipeline by using the command:
+Follow up on the progress.
+
+# Destroy
 
-    datajob destroy --stage dev --config datajob_stack.py
+Once the pipeline is finished you can pull down the pipeline by using the command:
+
+    datajob destroy --config datajob_stack.py
 
 As simple as that!
 
@@ -99,10 +103,6 @@ As simple as that!
 > datajob cli prints out the commands it uses in the back to build the pipeline.
 > If you want, you can use those.
 
-    cd examples/data_pipeline_simple
-    cdk deploy --app  "python datajob_stack.py"  -c stage=dev
-    cdk destroy --app  "python datajob_stack.py"  -c stage=dev
-
 # Ideas
 
 Any suggestions can be shared by starting a [discussion](https://github.com/vincentclaes/datajob/discussions)
@@ -122,4 +122,5 @@ These are the ideas, we find interesting to implement;
     - create sagemaker model
     - create sagemaker endpoint
     - expose sagemaker endpoint to the internet by levering lambda + api gateway
+
 - create a serverless UI that follows up on the different pipelines deployed on possibly different AWS accounts using Datajob