Steps to Implement ETL Processing with Dataflow
Step 1 : Enable Dataflow API
To enable “Dataflow API” firstly you have to create project in Google cloud Console and then search “API and Services” and click on enable API and Services.
Search “Dataflow API” in search bar then click on enable.
Step 2: Run given set of commands
Run the given set of commands in cloud shell to get dataflow
gsutil -m cp -R gs://spls/gsp290/dataflow-python-examples .
Set a variable in Cloud Shell equal to your project id now.
export PROJECT=
gcloud config set project $PROJECT
Step 3: Create Cloud Storage Bucket
Use these given set of commands to Create Cloud Storage Bucket
gsutil mb -c regional -l gs://$PROJECT
Step 4: Copy files in your bucket
Use these given set of commands to Copy files in your bucket
gsutil cp gs://spls/gsp290/data_files/usa_names.csv gs://$PROJECT/data_files/
gsutil cp gs://spls/gsp290/data_files/head_usa_names.csv gs://$PROJECT/data_files/
Step 5: Create the BigQuery ‘lake’ dataset
Construct a BigQuery dataset named “lake” using the Cloud Shell. Every table that you have in BigQuery will be loaded here:
bq mk lake
Step 6: Build a Dataflow pipeline
This is our final step to ingest data into the BigQuery table, you will establish an append-only Dataflow in this step.
Building Data Pipelines with Google Cloud Dataflow: ETL Processing
In today’s fast fast-moving world, businesses face the challenge of efficiently processing and transforming massive quantities of data into meaningful insights. Extract, Transform, Load (ETL) tactics play a vital function in this journey, enabling corporations to transform raw data into a structured and actionable format. Google Cloud gives a powerful solution for ETL processing called Dataflow, a completely managed and serverless data processing service. In this article, we will explore the key capabilities and advantages of ETL processing on Google Cloud and the use of Dataflow.
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