Data pipeline patterns in GCP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Cloud Storage bucket for raw data ingestion.

GCP

resource "google_storage_bucket" "raw_data_bucket" {
  name     = "my-raw-data-[1]"
  location = "US"
}

Drag options to blanks, or click blank then click option'

Abucket

Bstorage

Cpipeline

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using generic names like 'bucket' or 'storage' that don't describe the data.

Leaving the bucket name incomplete or too vague.

The bucket name should include a descriptive term like 'data' to indicate its purpose for raw data ingestion.

2fill in blank

medium

Complete the code to define a Pub/Sub topic for streaming data ingestion.

GCP

resource "google_pubsub_topic" "stream_topic" {
  name = "[1]-stream-topic"
}

Drag options to blanks, or click blank then click option'

Abatch

Bdata

Cstreaming

Draw

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'batch' which is for batch processing, not streaming.

Using vague names that don't indicate the topic's purpose.

The topic name should clearly indicate it is related to data streaming, so 'data' is a good base name.

3fill in blank

hard

Fix the error in the Dataflow job configuration to specify the correct runner.

GCP

resource "google_dataflow_job" "etl_job" {
  name     = "etl-job"
  template_gcs_path = "gs://dataflow-templates/latest/Word_Count"
  [1] = "DataflowRunner"
  region   = "us-central1"
}

Drag options to blanks, or click blank then click option'

Atemplate_type

Bjob_type

Crunner

Dexecution_mode

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect property names like 'job_type' or 'execution_mode'.

Omitting the runner property entirely.

The correct property to specify the runner in a Dataflow job is 'runner'.

4fill in blank

hard

Fill both blanks to create a BigQuery dataset with the correct location and description.

GCP

resource "google_bigquery_dataset" "analytics_dataset" {
  dataset_id = "analytics"
  location   = "[1]"
  description = "[2]"
}

Drag options to blanks, or click blank then click option'

AUS

BEU

CAnalytics data storage

DRaw data storage

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing the wrong location like 'EU' when the data is US-based.

Using a vague or incorrect description.

The dataset location should be 'US' for this example, and the description should clearly state its purpose as 'Analytics data storage'.

5fill in blank

hard

Fill all three blanks to define a Cloud Composer environment with the correct name, location, and machine type.

GCP

resource "google_composer_environment" "data_pipeline_env" {
  name   = "[1]"
  region = "[2]"
  config {
    node_config {
      machine_type = "[3]"
    }
  }
}

Drag options to blanks, or click blank then click option'

Adata-pipeline-env

Bus-central1

Cn1-standard-1

Deurope-west1

Attempts:

3 left

💡 Hint

Common Mistakes

Using a region that doesn't match the environment's location.

Choosing an unsupported machine type.

The environment name should be descriptive like 'data-pipeline-env', the region 'us-central1' is a common GCP region, and 'n1-standard-1' is a typical machine type for Composer nodes.

Practice

1. Which Google Cloud service is best suited for real-time messaging in a data pipeline?

easy

A. Cloud Storage

B. BigQuery

C. Pub/Sub

D. Cloud Functions

Solution

1. Step 1: Understand real-time messaging needs

   Real-time messaging requires a service that can quickly send and receive messages between components.

2. Step 2: Identify Google Cloud services for messaging

   Pub/Sub is designed for real-time messaging, while BigQuery is for analytics, Cloud Storage for file storage, and Cloud Functions for event-driven compute.

3. Final Answer:

   Pub/Sub -> Option C

4. Quick Check:

   Real-time messaging = Pub/Sub [OK]

Hint: Real-time messaging in GCP? Think Pub/Sub first [OK]

Common Mistakes:

• Confusing BigQuery as a messaging service
• Choosing Cloud Storage for streaming data
• Selecting Cloud Functions instead of messaging service

2. Which of the following is the correct syntax to create a Dataflow pipeline in Python using Apache Beam?

easy

A. pipeline = beam.Pipeline(options=options)

B. pipeline = Dataflow(options)

C. pipeline = beam.Create(options)

D. pipeline = DataflowPipeline(options=options)

Solution

1. Step 1: Recall Apache Beam pipeline creation syntax

   Apache Beam pipelines are created using beam.Pipeline with options passed as a parameter.

2. Step 2: Compare options

   pipeline = beam.Pipeline(options=options) matches the correct syntax. Other options use incorrect class names or methods.

3. Final Answer:

   pipeline = beam.Pipeline(options=options) -> Option A

4. Quick Check:

   Apache Beam pipeline creation = beam.Pipeline [OK]

Hint: Beam pipelines start with beam.Pipeline(options=...) [OK]

Common Mistakes:

• Using Dataflow instead of beam.Pipeline
• Confusing Create with pipeline creation
• Incorrect class names like DataflowPipeline

3. Given this simplified Dataflow pipeline code snippet, what will be the output in BigQuery?

p = beam.Pipeline(options=options)
(p
 | 'Read' >> beam.io.ReadFromPubSub(subscription='sub')
 | 'Parse' >> beam.Map(lambda x: x.decode('utf-8'))
 | 'Write' >> beam.io.WriteToBigQuery('project:dataset.table'))
p.run()

medium

A. Data will be stored in Cloud Storage instead of BigQuery

B. Data from Pub/Sub messages decoded and stored in BigQuery table

C. Pipeline will fail due to missing schema in WriteToBigQuery

D. Raw Pub/Sub messages stored as bytes in BigQuery

Solution

1. Step 1: Analyze pipeline steps

   The pipeline reads messages from Pub/Sub, decodes bytes to strings, then writes to BigQuery.

2. Step 2: Understand output behavior

   Since messages are decoded before writing, the data stored in BigQuery will be the decoded string form.

3. Final Answer:

   Data from Pub/Sub messages decoded and stored in BigQuery table -> Option B

4. Quick Check:

   Decoded Pub/Sub data written to BigQuery = Data from Pub/Sub messages decoded and stored in BigQuery table [OK]

Hint: Decoded Pub/Sub data goes to BigQuery if WriteToBigQuery used [OK]

Common Mistakes:

• Assuming raw bytes are stored without decoding
• Thinking pipeline fails without explicit schema (default schema can be inferred)
• Confusing output destination as Cloud Storage

4. You have a Dataflow pipeline that reads from Pub/Sub but fails with a permission error. What is the most likely cause?

medium

A. The Pub/Sub topic does not exist

B. BigQuery dataset is not created

C. The Dataflow pipeline code has syntax errors

D. The service account running Dataflow lacks Pub/Sub Subscriber role

Solution

1. Step 1: Identify permission error context

   Permission errors usually mean the service account lacks rights to access a resource.

2. Step 2: Match error to resource

   Since the pipeline reads from Pub/Sub, the service account needs the Subscriber role on the Pub/Sub topic.

3. Final Answer:

   The service account running Dataflow lacks Pub/Sub Subscriber role -> Option D

4. Quick Check:

   Permission error reading Pub/Sub = missing Subscriber role [OK]

Hint: Permission errors often mean missing roles on resources [OK]

Common Mistakes:

• Assuming syntax errors cause permission errors
• Thinking topic absence causes permission errors
• Confusing BigQuery dataset issues with Pub/Sub permissions

5. You want to build a data pipeline that ingests streaming IoT sensor data, processes it in real-time, and stores aggregated results hourly in BigQuery. Which pattern best fits this use case?

hard

A. Use Pub/Sub for ingestion, Dataflow streaming for processing with windowing, then write to BigQuery

B. Use Cloud Storage for ingestion, batch Dataflow jobs for processing, then write to BigQuery

C. Use BigQuery streaming inserts directly from IoT devices

D. Use Cloud Functions triggered by Cloud Storage uploads to process data

Solution

1. Step 1: Identify streaming ingestion and processing needs

   IoT sensor data is streaming and requires real-time processing with time-based aggregation.

2. Step 2: Match GCP services to requirements

   Pub/Sub handles streaming ingestion, Dataflow streaming supports windowed aggregation, and BigQuery stores results efficiently.

3. Final Answer:

   Use Pub/Sub for ingestion, Dataflow streaming for processing with windowing, then write to BigQuery -> Option A

4. Quick Check:

   Streaming ingestion + windowed processing + BigQuery = Use Pub/Sub for ingestion, Dataflow streaming for processing with windowing, then write to BigQuery [OK]

Hint: Streaming + windowed aggregation = Pub/Sub + Dataflow + BigQuery [OK]

Common Mistakes:

• Using batch processing for streaming data
• Trying to write streaming data directly to BigQuery without processing
• Using Cloud Storage which is for batch files, not streaming