{"id":9078,"date":"2021-05-30T11:55:53","date_gmt":"2021-05-30T11:55:53","guid":{"rendered":"https:\/\/prwatech.in\/blog\/?p=9078"},"modified":"2023-07-13T11:47:41","modified_gmt":"2023-07-13T11:47:41","slug":"working-with-dataflow","status":"publish","type":"post","link":"https:\/\/prwatech.in\/blog\/google-cloud-platform\/dataflow\/working-with-dataflow\/","title":{"rendered":"Working with Dataflow"},"content":{"rendered":"\r\n

Prerequisites<\/strong><\/p>\r\n\r\n\r\n\r\n

GCP<\/a> account<\/p>\r\n\r\n\r\n\r\n

Open Console.<\/p>\r\n\r\n\r\n\r\n

Click on activate cloud shell<\/p>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 git clone https:\/\/github.com\/GoogleCloudPlatform\/training-data-analyst<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ls<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

Create bucket in console<\/a>. Give bucket name as same as the project ID<\/p>\r\n\r\n\r\n\r\n

In shell, execute the below command<\/p>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 BUCKET=”<bucket-name>”<\/p>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 echo $BUCKET<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

Open Menu > API services > Library<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

Search Dataflow. Click Dataflow API<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

Click Enable<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 cd training-data-analyst\/courses\/data_analysis\/lab2\/python<\/p>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ls<\/p>\r\n\r\n\r\n\r\n

The files will be displayed<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 nano install_packages.sh\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 #open the file install_packages.sh<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

The file contents can be shown. This file is to install the components.<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 sudo .\/install_packages.sh<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

To check python version<\/p>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pip-V<\/p>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pip3 -V<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 nano grep.py\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Open the file grep.py and check the content<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n
\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 python3 grep.py<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ls \/tmp\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 #It will display whether the file is executed or not.<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 cat \/tmp\/output-*\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 #It will display detailed output.<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 gsutil cp ..\/javahelp\/src\/main\/java\/com\/google\/cloud\/training\/dataanalyst\/javahelp\/*.java gs:\/\/$BUCKET\/javahelp<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

Check the file is saved or not.<\/p>\r\n\r\n\r\n\r\n

Open Menu > Cloud Storage.<\/p>\r\n\r\n\r\n\r\n

Open Bucket.<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

The file will be copied or not.<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 echo $DEVSHELL_PROJECT_ID $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/p>\r\n\r\n\r\n\r\n

echo $BUCKET<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 nano grepc.py<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

Edit the file.<\/p>\r\n\r\n\r\n\r\n

PROJECT='<project_ID>’<\/p>\r\n\r\n\r\n\r\n

BUCKET='<bucket_name>’<\/p>\r\n\r\n\r\n\r\n

NB : If the Project ID and Bucket is same, we can give the same ID<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

To Save and exit. Press \u2018Ctrl + X\u2019. Press \u2018Y\u2019 and \u2018Enter\u2019<\/p>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 python3 grepc.py\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 #Execute file grepc.py<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

Open Console >Dataflow > Jobs<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

Open the Job which is executed.<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

Click the Job Graph.<\/p>\r\n\r\n\r\n\r\n

The Graph is displayed.<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

In Job Graph on right side you can see the Job info and resource metrics.<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

Open Shell.<\/p>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ls<\/p>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 nano is_popular.py<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

It will open the file is_popular.py<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 python3 .\/is_popular.py\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 #To execute theis_popular.py file<\/p>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 cat \/tmp\/output-*\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 #Display the output<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 python3 .\/is_popular.py\u00a0 –output_prefix=\/tmp\/myoutput<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

$\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 nano \/tmp\/myoutput-00000-of-00001<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

It will open the file with output.<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

Open Menu > Cloud Storage.<\/p>\r\n\r\n\r\n\r\n

Open Bucket.<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

Open javahelp\/ folder<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n\r\n\r\n\r\n

The outputs will be stored in it.<\/p>\r\n\r\n\r\n\r\n

\"\"<\/figure>\r\n","protected":false},"excerpt":{"rendered":"

Prerequisites GCP account Open Console. Click on activate cloud shell $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 git clone https:\/\/github.com\/GoogleCloudPlatform\/training-data-analyst $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ls Create bucket in console. Give bucket name as same as the project ID In shell, execute the below command $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 BUCKET=”<bucket-name>” $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 echo $BUCKET Open Menu > API services > Library Search Dataflow. Click Dataflow API Click Enable $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1636,1],"tags":[1426,1427,605,699,700,617,683,684,685,611,1400,692],"class_list":["post-9078","post","type-post","status-publish","format-standard","hentry","category-dataflow","category-google-cloud-platform","tag-dataflow","tag-dataflow-gcp","tag-gcp","tag-gcp-certification","tag-gcp-cloud-console","tag-google-cloud","tag-google-cloud-certification","tag-google-cloud-console","tag-google-cloud-courses","tag-google-cloud-platform","tag-google-cloud-platform-tutorial","tag-google-cloud-training"],"acf":[],"yoast_head":"\nWorking with Dataflow - Prwatech<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Working with Dataflow - Prwatech\" \/>\n<meta property=\"og:description\" content=\"Prerequisites GCP account Open Console. Click on activate cloud shell $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 git clone https:\/\/github.com\/GoogleCloudPlatform\/training-data-analyst $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ls Create bucket in console. Give bucket name as same as the project ID In shell, execute the below command $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 BUCKET=”<bucket-name>” $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 echo $BUCKET Open Menu > API services > Library Search Dataflow. 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Click on activate cloud shell $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 git clone https:\/\/github.com\/GoogleCloudPlatform\/training-data-analyst $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ls Create bucket in console. Give bucket name as same as the project ID In shell, execute the below command $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 BUCKET=”<bucket-name>” $\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 echo $BUCKET Open Menu > API services > Library Search Dataflow. 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