Data pipeline patterns in GCP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a data pipeline in cloud computing?

A data pipeline is a set of steps that move and process data from one place to another, often automatically and continuously, like a water pipe moving water from a source to a destination.

beginner

What is the 'batch processing' pattern in data pipelines?

Batch processing collects data over time and processes it all at once, like baking a big batch of cookies instead of one at a time.

beginner

Explain the 'streaming processing' pattern in data pipelines.

Streaming processing handles data continuously as it arrives, like a conveyor belt where items are processed one by one without waiting.

intermediate

What is the role of Google Cloud Pub/Sub in data pipelines?

Google Cloud Pub/Sub acts like a message bus that moves data messages between services quickly and reliably, supporting streaming data pipelines.

intermediate

Why use Dataflow in a data pipeline?

Dataflow runs data processing tasks that can handle both batch and streaming data, automatically scaling and managing resources so you don't have to worry about the details.

Which data pipeline pattern processes data in real-time as it arrives?

A Streaming processing

B Batch processing

C Manual processing

D Archival processing

Streaming processing handles data continuously as it arrives, enabling real-time data handling.

What GCP service is best for messaging between components in a streaming pipeline?

A Cloud Storage

B BigQuery

C Cloud Pub/Sub

D Cloud SQL

Cloud Pub/Sub is designed for messaging and event delivery in streaming pipelines.

Which service can handle both batch and streaming data processing in GCP?

A Dataflow

B Cloud Functions

C Cloud Run

D Cloud Spanner

Dataflow supports unified batch and streaming data processing.

Batch processing is best described as:

A Processing data one piece at a time immediately

B Ignoring data until manually triggered

C Processing data only during business hours

D Collecting data and processing it all at once

Batch processing collects data over time and processes it together.

Which pattern would you choose for a pipeline that needs to analyze sensor data instantly?

A Batch processing

B Streaming processing

C Archival processing

D Manual processing

Streaming processing is suitable for instant analysis of continuous data like sensor data.

Describe the main differences between batch and streaming data pipeline patterns.

Think about how and when data is processed.

Explain how Google Cloud services like Pub/Sub and Dataflow work together in a streaming data pipeline.

Consider messaging and processing roles.

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