Cloud Dataflow: Building Data Processing Pipelines

Cloud Dataflow offers a powerful platform for building scalable and reliable data processing pipelines within Google Cloud. These pipelines enable organizations to ingest, transform, and analyze large volumes of data in real-time, providing valuable insights and driving informed decision-making.

At its core, Cloud Dataflow simplifies the development and management of data pipelines by abstracting away the complexities of distributed computing infrastructure. Developers can focus on defining the data processing logic using familiar programming languages and libraries, while Dataflow handles the underlying infrastructure provisioning, scaling, and optimization.

Prerequisites

GCP account

Open Console.

Open Menu > BigQuery

In Query Editor, Paste the below query.

SELECT

 content

FROM

 fh-bigquery.github_extracts.contents_java_2016

LIMIT

 10

Click Run.

It will display the results.

Paste the below query.

SELECT

 COUNT(*)

FROM

 fh-bigquery.github_extracts.contents_java_2016

Click Run.

It will give the query result.

Click on activate cloud shell

 git clone https://github.com/GoogleCloudPlatform/training-data-analyst

 ls

Create bucket in console. Give bucket name as same as the project ID

In shell, execute the below command

BUCKET=”<bucket-name>”

 echo $BUCKET

      cd training-data-analyst/courses/data_analysis/lab2/python

       ls

The files will be displayed

     nano JavaProjectsThatNeedHelp.py

It will open the file. Ctrl + x to exit

python3 JavaProjectsThatNeedHelp.py –bucket $BUCKET –project $DEVSHELL_PROJECT_ID –DataFlowRunner

Go to DataFlow > Jobs The jobs will be running

Open the running job

See the dataflow running

After running, It will shown as succeeded

After execution, Go to bucket.

Open javahelp/ folder

The Result will be stored in it.