Dataflow for stream/batch processing in GCP - Mini Project: Build & Apply

Dataflow for stream/batch processing

📖 Scenario: You work for a company that collects website visit data. You want to process this data using Google Cloud Dataflow. Sometimes data comes in batches, and sometimes it streams in real-time. You will create a simple Dataflow pipeline configuration to handle both batch and streaming data.

🎯 Goal: Build a Google Cloud Dataflow pipeline configuration that can run in both batch and streaming modes by setting up the input source, pipeline options, and the runner configuration.

📋 What You'll Learn

Create a pipeline options dictionary with the project ID and region

Add a boolean configuration to specify streaming mode

Configure the input source as a Pub/Sub subscription for streaming or a Cloud Storage path for batch

Complete the pipeline options with the runner and input source

💡 Why This Matters

🌍 Real World

Dataflow pipelines are used to process large amounts of data in real-time or batch mode for analytics, monitoring, and reporting.

💼 Career

Understanding how to configure Dataflow pipelines is essential for cloud engineers and data engineers working with Google Cloud Platform.

1

Create initial pipeline options dictionary

Create a dictionary called pipeline_options with these exact entries: 'project': 'my-gcp-project' and 'region': 'us-central1'.

GCP

# Your code here

Hint

Use a Python dictionary with keys 'project' and 'region' and their exact values.

2

Add streaming mode configuration

Add a boolean key 'streaming' with the value True to the existing pipeline_options dictionary.

GCP

pipeline_options = {'project': 'my-gcp-project', 'region': 'us-central1'}
# Add 'streaming': True to pipeline_options

Hint

Add the key 'streaming' with value True to the dictionary using square brackets.

3

Configure input source based on streaming mode

Create a variable called input_source. Set it to the string 'projects/my-gcp-project/subscriptions/my-subscription' if pipeline_options['streaming'] is True. Otherwise, set it to the string 'gs://my-bucket/data/*.json'.

GCP

pipeline_options = {'project': 'my-gcp-project', 'region': 'us-central1'}
pipeline_options['streaming'] = True
# Set input_source based on pipeline_options['streaming']

Hint

Use a conditional expression to assign input_source based on the streaming flag.

4

Complete pipeline options with runner and input source

Add the key 'runner' with value 'DataflowRunner' and the key 'input' with the value of input_source to the pipeline_options dictionary.

GCP

pipeline_options = {'project': 'my-gcp-project', 'region': 'us-central1'}
pipeline_options['streaming'] = True
input_source = 'projects/my-gcp-project/subscriptions/my-subscription' if pipeline_options['streaming'] else 'gs://my-bucket/data/*.json'
# Add 'runner' and 'input' keys to pipeline_options

Hint

Add the keys 'runner' and 'input' to the dictionary with the specified values.

Practice

1. What is the main difference between streaming and batch processing in Google Cloud Dataflow?

easy

A. Batch processing is faster than streaming in all cases.

B. Streaming processes data continuously as it arrives; batch processes fixed sets of data.

C. Streaming only reads data; batch only writes data.

D. Batch processes data continuously; streaming processes fixed sets of data.

Solution

1. Step 1: Understand streaming processing

   Streaming processes data continuously as new data arrives in real time.

2. Step 2: Understand batch processing

   Batch processes data in fixed sets or chunks, not continuously.

3. Final Answer:

   Streaming processes data continuously as it arrives; batch processes fixed sets of data. -> Option B

4. Quick Check:

   Streaming = continuous, Batch = fixed sets [OK]

Hint: Streaming = live data; batch = fixed data sets [OK]

Common Mistakes:

• Confusing batch as continuous processing
• Thinking streaming only reads data
• Assuming batch is always faster

2. Which option correctly sets a Dataflow pipeline to run in streaming mode using the Python SDK?

easy

A. options.view_as(StandardOptions).batch = True

B. options.view_as(StandardOptions).stream = False

C. options.view_as(StandardOptions).mode = 'batch'

D. options.view_as(StandardOptions).streaming = True

Solution

1. Step 1: Identify the correct property for streaming

   In Dataflow Python SDK, streaming mode is enabled by setting streaming = True on StandardOptions.

2. Step 2: Check other options

   Batch is default and does not require setting batch = True; mode property does not exist; stream = False disables streaming.

3. Final Answer:

   options.view_as(StandardOptions).streaming = True -> Option D

4. Quick Check:

   streaming = True enables streaming mode [OK]

Hint: Set streaming=True in StandardOptions for streaming mode [OK]

Common Mistakes:

• Using batch=True which is invalid
• Setting mode='batch' which is not a valid property
• Confusing stream with streaming property

3. Given this Python Dataflow pipeline snippet, what will be the output if run in batch mode on input [1, 2, 3]?

import apache_beam as beam
with beam.Pipeline() as p:
  result = (
    p | beam.Create([1, 2, 3])
      | beam.Map(lambda x: x * 2)
      | beam.Map(lambda x: x + 1)
  )
  result | beam.Map(print)

medium

A. 3, 5, 7

B. 1, 2, 3

C. 2, 4, 6

D. Error due to missing streaming option

Solution

1. Step 1: Trace the transformations

   Input list is [1, 2, 3]. First Map multiplies each by 2: [2, 4, 6]. Second Map adds 1 to each: [3, 5, 7].

2. Step 2: Confirm batch mode output

   Batch mode processes all data at once, so output will be printed as 3, 5, 7.

3. Final Answer:

   3, 5, 7 -> Option A

4. Quick Check:

   Multiply by 2 then add 1 = 3,5,7 [OK]

Hint: Apply transformations stepwise to input list [OK]

Common Mistakes:

• Forgetting to multiply before adding
• Assuming streaming mode affects output here
• Expecting original input as output

4. You wrote a Dataflow streaming pipeline but it fails to process live data. Which fix is most likely correct?

medium

A. Set options.view_as(StandardOptions).streaming = True before running the pipeline.

B. Change the input source to a batch file instead of a streaming source.

C. Remove all Map transforms to simplify the pipeline.

D. Run the pipeline without specifying any options.

Solution

1. Step 1: Identify streaming mode requirement

   Streaming pipelines require setting streaming=True in options to process live data.

2. Step 2: Evaluate other options

   Changing input to batch file disables streaming; removing transforms breaks logic; no options means default batch mode.

3. Final Answer:

   Set options.view_as(StandardOptions).streaming = True before running the pipeline. -> Option A

4. Quick Check:

   Enable streaming option to fix live data processing [OK]

Hint: Enable streaming=True to fix live data issues [OK]

Common Mistakes:

• Not enabling streaming mode in options
• Switching to batch input source mistakenly
• Removing necessary transforms

5. You want to build a Dataflow pipeline that reads live sensor data, filters out values below 50, and writes results to BigQuery. Which approach is best?

hard

A. Use streaming mode with a Cloud Storage file source, apply a Map transform, then write to BigQuery sink.

B. Use batch mode with a Cloud Storage file source, apply a Map transform, then write to BigQuery sink.

C. Use streaming mode with a Pub/Sub source, apply a Filter transform, then write to BigQuery sink.

D. Use batch mode with a Pub/Sub source, apply a Filter transform, then write to BigQuery sink.

Solution

1. Step 1: Choose streaming mode for live sensor data

   Live sensor data requires streaming mode to process data as it arrives.

2. Step 2: Select Pub/Sub as source and apply Filter transform

   Pub/Sub is the streaming data source; Filter removes values below 50; BigQuery sink stores results.

3. Final Answer:

   Use streaming mode with a Pub/Sub source, apply a Filter transform, then write to BigQuery sink. -> Option C

4. Quick Check:

   Streaming + Pub/Sub + Filter + BigQuery = correct pipeline [OK]

Hint: Live data needs streaming + Pub/Sub + Filter + BigQuery [OK]

Common Mistakes:

• Using batch mode for live data
• Using Cloud Storage as streaming source
• Applying Map instead of Filter for filtering