GCP operators (BigQuery, GCS, Dataflow) in Apache Airflow - Step-by-Step Execution

Process Flow - GCP operators (BigQuery, GCS, Dataflow)

Start DAG

↓

Trigger GCS Operator

↓

Trigger BigQuery Operator

↓

Trigger Dataflow Operator

↓

Check Task Status

↓

Complete DAG

The DAG starts, triggers GCS, then BigQuery, then Dataflow operators in order, checks their status, and completes.

Execution Sample

Apache Airflow

from airflow import DAG
from airflow.providers.google.cloud.operators.bigquery import BigQueryInsertJobOperator
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator
from airflow.providers.google.cloud.operators.dataflow import DataflowStartFlexTemplateOperator

with DAG('gcp_operators_dag', schedule_interval=None, start_date=None, catchup=False) as dag:
    create_bucket = GCSCreateBucketOperator(task_id='create_bucket', bucket_name='my-bucket')
    run_bq = BigQueryInsertJobOperator(task_id='run_bq', configuration={"query": {"query": "SELECT 1", "useLegacySql": False}})
    start_dataflow = DataflowStartFlexTemplateOperator(task_id='start_dataflow', body={"launchParameter": {"jobName": "my-job", "containerSpecGcsPath": "gs://dataflow-templates/latest/flex/Word_Count"}})

    create_bucket >> run_bq >> start_dataflow

This Airflow DAG creates a GCS bucket, runs a BigQuery query, then starts a Dataflow job in sequence.

Process Table

Step	Task Triggered	Action	Status	Next Task
1	create_bucket	Create GCS bucket 'my-bucket'	Success	run_bq
2	run_bq	Run BigQuery SQL query 'SELECT 1'	Success	start_dataflow
3	start_dataflow	Start Dataflow job 'my-job' with flex template	Success	None
4	DAG	All tasks completed successfully	Success	End

💡 All tasks executed successfully in order, DAG run completes.

Status Tracker

Variable	Start	After create_bucket	After run_bq	After start_dataflow
GCS Bucket	None	my-bucket created	my-bucket exists	my-bucket exists
BigQuery Job	None	None	Query job completed	Query job completed
Dataflow Job	None	None	None	Dataflow job started

Key Moments - 3 Insights

Why does the Dataflow operator run only after BigQuery finishes?

Because the DAG defines dependencies with 'create_bucket >> run_bq >> start_dataflow', so each task waits for the previous to succeed (see execution_table steps 2 and 3).

What happens if the GCS bucket creation fails?

The DAG stops and does not run BigQuery or Dataflow tasks, since downstream tasks depend on the success of previous ones (see execution_table step 1).

How does Airflow know the status of each GCP operator?

Each operator reports success or failure after execution, which Airflow tracks to decide whether to proceed (see 'Status' column in execution_table).

Visual Quiz - 3 Questions

Look at the execution_table, what is the status of the BigQuery task at step 2?

A Failed

B Success

C Running

D Skipped

💡 Hint

Check the 'Status' column for step 2 in the execution_table.

At which step does the Dataflow job start according to the execution_table?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look for 'start_dataflow' task in the 'Task Triggered' column.

If the GCS bucket creation failed, what would happen to the BigQuery task?

A It would be skipped

B It would run anyway

C It would run after Dataflow

D It would retry immediately

💡 Hint

Refer to key_moments about task dependencies and failure impact.

Concept Snapshot

GCP Operators in Airflow:
- GCSCreateBucketOperator creates buckets
- BigQueryInsertJobOperator runs SQL queries
- DataflowStartFlexTemplateOperator starts Dataflow jobs
- Define task order with >> to set dependencies
- Airflow tracks success/failure to control flow

Full Transcript

This visual execution shows an Airflow DAG using GCP operators: first creating a GCS bucket, then running a BigQuery query, and finally starting a Dataflow job. Each task waits for the previous to succeed before running. The execution table tracks each step's action and status. Variables like bucket existence and job completion update after each task. Key moments clarify why tasks run in order and what happens on failure. The quiz tests understanding of task status and dependencies. This helps beginners see how Airflow manages GCP tasks step-by-step.

Practice

1. What is the main purpose of GCP operators in Airflow when working with BigQuery, GCS, and Dataflow?

easy

A. To create virtual machines in Google Cloud

B. To manually write SQL queries for BigQuery

C. To replace Google Cloud Console for all cloud services

D. To automate and manage cloud tasks like queries, storage, and data processing

Solution

1. Step 1: Understand the role of GCP operators in Airflow

   GCP operators are designed to automate cloud tasks such as running queries in BigQuery, managing files in GCS, and launching Dataflow jobs.

2. Step 2: Identify the correct purpose among options

   Only To automate and manage cloud tasks like queries, storage, and data processing correctly describes automation and management of cloud tasks, while others describe unrelated or manual tasks.

3. Final Answer:

   To automate and manage cloud tasks like queries, storage, and data processing -> Option D

4. Quick Check:

   GCP operators automate cloud tasks = A [OK]

Hint: GCP operators automate cloud tasks, not manual or unrelated actions [OK]

Common Mistakes:

• Thinking GCP operators replace all Google Cloud Console functions
• Confusing manual SQL writing with automation
• Assuming GCP operators create virtual machines

2. Which of the following is the correct way to import the BigQuery operator in an Airflow DAG?

easy

A. from airflow.providers.google.cloud.operators.bigquery import BigQueryInsertJobOperator

B. import airflow.gcp.bigquery as bq

C. from airflow.operators.bigquery import BigQueryOperator

D. from google.cloud import bigquery

Solution

1. Step 1: Recall the correct import path for BigQuery operators in Airflow

   Airflow uses provider packages; the BigQuery operator is imported from airflow.providers.google.cloud.operators.bigquery.

2. Step 2: Match the correct operator class and import syntax

   from airflow.providers.google.cloud.operators.bigquery import BigQueryInsertJobOperator uses the correct module and operator class name. Options B and C use incorrect paths or outdated names. from google.cloud import bigquery imports the Google Cloud client library, not Airflow operator.

3. Final Answer:

   from airflow.providers.google.cloud.operators.bigquery import BigQueryInsertJobOperator -> Option A

4. Quick Check:

   Correct Airflow BigQuery import = D [OK]

Hint: Airflow GCP operators import from airflow.providers.google.cloud.operators [OK]

Common Mistakes:

• Using outdated or incorrect import paths
• Confusing Google Cloud client libraries with Airflow operators
• Importing non-existent modules

3. Given this Airflow task using BigQueryInsertJobOperator:

bigquery_task = BigQueryInsertJobOperator(
    task_id='run_query',
    configuration={
        'query': {
            'query': 'SELECT COUNT(*) FROM `my_dataset.my_table`',
            'useLegacySql': False
        }
    }
)

What will this task do when executed?

medium

A. Upload data from GCS to BigQuery

B. Create a new BigQuery table named my_table

C. Run a BigQuery SQL query that counts rows in my_dataset.my_table

D. Delete the table my_dataset.my_table

Solution

1. Step 1: Analyze the BigQueryInsertJobOperator configuration

   The configuration specifies a SQL query: 'SELECT COUNT(*) FROM `my_dataset.my_table`' with standard SQL enabled (useLegacySql: False).

2. Step 2: Determine the task action from the query

   The query counts rows in the specified table. The operator runs this query as a job in BigQuery.

3. Final Answer:

   Run a BigQuery SQL query that counts rows in my_dataset.my_table -> Option C

4. Quick Check:

   BigQueryInsertJobOperator runs SQL queries = A [OK]

Hint: Look at the 'query' field to see what SQL runs [OK]

Common Mistakes:

• Assuming it creates or deletes tables
• Confusing query execution with data upload
• Ignoring useLegacySql flag

4. You wrote this Airflow task to upload a file to GCS:

upload_task = GCSToBigQueryOperator(
    task_id='upload_to_gcs',
    bucket='my_bucket',
    source_objects=['data.csv'],
    destination_project_dataset_table='my_dataset.my_table'
)

What is wrong with this task?

medium

A. GCSToBigQueryOperator is used incorrectly to upload to GCS; it loads data from GCS to BigQuery

B. The bucket name must be omitted for this operator

C. source_objects should be a string, not a list

D. destination_project_dataset_table should be a GCS path

Solution

1. Step 1: Understand GCSToBigQueryOperator purpose

   This operator loads data from Google Cloud Storage (GCS) into BigQuery tables; it does not upload files to GCS.

2. Step 2: Identify the misuse in the task

   The task tries to upload a file to GCS using GCSToBigQueryOperator, which is incorrect. Uploading to GCS requires a different operator like GCSUploadOperator.

3. Final Answer:

   GCSToBigQueryOperator is used incorrectly to upload to GCS; it loads data from GCS to BigQuery -> Option A

4. Quick Check:

   GCSToBigQueryOperator loads data from GCS, not upload to GCS [OK]

Hint: Check operator purpose: GCSToBigQuery loads data, doesn't upload files [OK]

Common Mistakes:

• Using GCSToBigQueryOperator to upload files to GCS
• Confusing source_objects type
• Misunderstanding destination table format

5. You want to run a Dataflow job in Airflow that reads data from GCS, processes it, and writes results back to BigQuery. Which combination of operators is best to build this workflow?

hard

A. Use DataflowCreatePythonJobOperator to run the Dataflow job, and BigQueryInsertJobOperator to run SQL queries

B. Use DataflowCreatePythonJobOperator to run Dataflow job that reads/writes data, no separate BigQuery operator needed

C. Use DataflowCreateJavaJobOperator to run Dataflow, and GCSToBigQueryOperator to load data from GCS to BigQuery

D. Use GCSCreateBucketOperator to create buckets, then DataflowTemplatedJobStartOperator to run Dataflow

Solution

1. Step 1: Understand Dataflow job capabilities

   Dataflow jobs can read from GCS, process data, and write directly to BigQuery within the same job.

2. Step 2: Choose the operator that runs the Dataflow job end-to-end

   DataflowCreatePythonJobOperator runs Python Dataflow jobs that handle reading and writing data, so no separate BigQuery operator is needed for writing results.

3. Step 3: Evaluate other options

   Use DataflowCreatePythonJobOperator to run the Dataflow job, and BigQueryInsertJobOperator to run SQL queries runs Dataflow and BigQuery separately, which is unnecessary if Dataflow handles both. Use GCSCreateBucketOperator to create buckets, then DataflowTemplatedJobStartOperator to run Dataflow involves bucket creation, not relevant here. Use DataflowCreateJavaJobOperator to run Dataflow, and GCSToBigQueryOperator to load data from GCS to BigQuery uses Java job and GCSToBigQueryOperator, which is redundant.

4. Final Answer:

   Use DataflowCreatePythonJobOperator to run Dataflow job that reads/writes data, no separate BigQuery operator needed -> Option B

5. Quick Check:

   Dataflow job can read/write data itself = B [OK]

Hint: Dataflow jobs can handle GCS to BigQuery in one step [OK]

Common Mistakes:

• Running separate BigQuery operators unnecessarily
• Confusing Dataflow Java and Python operators
• Adding unrelated bucket creation tasks