Data Fusion for ETL in GCP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a new Data Fusion instance in GCP.

GCP

gcloud datafusion instances create [1] --location=us-central1 --type=BASIC

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

Acreate-instance

Bmy-instance

Cdatafusion1

Dinstance123

Attempts:

3 left

💡 Hint

Common Mistakes

Using a command keyword instead of an instance name.

Including spaces or special characters in the instance name.

The command requires the instance name as the first argument. 'my-instance' is a valid name.

2fill in blank

medium

Complete the code to start a pipeline run in Data Fusion.

GCP

gcloud datafusion pipelines run [1] etl-pipeline --location=us-central1

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

Adefault-instance

Bpipeline-runner

Cfusion-instance

Dmy-instance

Attempts:

3 left

💡 Hint

Common Mistakes

Using a generic or incorrect instance name.

Confusing pipeline name with instance name.

The instance name must match the Data Fusion instance where the pipeline is deployed. 'my-instance' is correct here.

3fill in blank

hard

Fix the error in the pipeline configuration to specify the source plugin correctly.

GCP

"source": {"name": "[1]", "type": "plugin"}

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

ABigQuerySource

BBigQuerySink

CGCSFile

DPubSubSource

Attempts:

3 left

💡 Hint

Common Mistakes

Using a sink plugin name for the source.

Confusing GCS or Pub/Sub plugins with BigQuery.

The source plugin for reading from BigQuery is 'BigQuerySource'. 'BigQuerySink' is for writing data.

4fill in blank

hard

Fill both blanks to filter records where the 'age' field is greater than 30 in the Wrangler transform.

GCP

"filterCondition": "[1] [2] 30"

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

Aage

B>

C<

Dage >

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>'.

Combining field and operator in one blank.

The filter condition requires the field name 'age' followed by the greater than operator '>'.

5fill in blank

hard

Fill all three blanks to define a pipeline schedule that runs daily at midnight.

GCP

"schedule": {"type": "[1]", "startTime": "[2]", "interval": "[3]"}

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

Atime-based

B00:00

C24h

Devent-based

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'event-based' instead of 'time-based'.

Incorrect time format for startTime.

Wrong interval value for daily schedule.

The schedule type for time-based runs is 'time-based'. The start time at midnight is '00:00', and the interval for daily runs is '24h'.

Practice

1. What is the main purpose of Google Cloud Data Fusion in ETL processes?

easy

A. To store large amounts of raw data permanently

B. To write complex SQL queries for data analysis

C. To visually build and automate data pipelines without coding

D. To create virtual machines for data processing

Solution

1. Step 1: Understand Data Fusion's role

   Data Fusion is designed to help users build data pipelines visually, making ETL easier without needing to write code.

2. Step 2: Compare options

   Options A, B, and D describe other tools or services, not Data Fusion's main purpose.

3. Final Answer:

   To visually build and automate data pipelines without coding -> Option C

4. Quick Check:

   Data Fusion = Visual ETL pipelines [OK]

Hint: Data Fusion is about visual pipelines, not coding or storage [OK]

Common Mistakes:

• Confusing Data Fusion with BigQuery for SQL queries
• Thinking Data Fusion stores data permanently
• Assuming Data Fusion manages virtual machines

2. Which of the following is the correct way to add a source plugin in a Data Fusion pipeline?

easy

A. Drag the source plugin from the palette and drop it onto the canvas

B. Write a SQL query to define the source

C. Create a virtual machine and install the source plugin manually

D. Use the command line to start the source plugin

Solution

1. Step 1: Recall Data Fusion UI usage

   Data Fusion uses a visual interface where you drag and drop plugins to build pipelines.

2. Step 2: Eliminate incorrect methods

   Options A, C, and D involve coding, manual installs, or command line, which are not how Data Fusion works for adding plugins.

3. Final Answer:

   Drag the source plugin from the palette and drop it onto the canvas -> Option A

4. Quick Check:

   Data Fusion = Drag & drop plugins [OK]

Hint: Data Fusion uses drag-and-drop for plugins, not code [OK]

Common Mistakes:

• Trying to write SQL to add plugins
• Thinking manual VM setup is needed
• Using command line instead of UI

3. Given a Data Fusion pipeline with a source plugin reading from Cloud Storage and a sink plugin writing to BigQuery, what happens when the pipeline runs successfully?

medium

A. The pipeline deletes data in BigQuery

B. Data is moved from Cloud Storage to BigQuery after any transformations

C. The pipeline creates a new Cloud Storage bucket

D. Data is copied from BigQuery to Cloud Storage

Solution

1. Step 1: Understand source and sink roles

   The source plugin reads data from Cloud Storage, and the sink plugin writes data to BigQuery.

2. Step 2: Identify pipeline behavior

   When the pipeline runs, data flows from source to sink, optionally transformed in between.

3. Final Answer:

   Data is moved from Cloud Storage to BigQuery after any transformations -> Option B

4. Quick Check:

   Source to sink = Cloud Storage to BigQuery [OK]

Hint: Source reads, sink writes; data flows forward [OK]

Common Mistakes:

• Confusing source and sink directions
• Thinking pipeline creates buckets automatically
• Assuming pipeline deletes data

4. You created a Data Fusion pipeline but it fails to start. Which of the following is the most likely cause?

medium

A. The pipeline has too many source plugins

B. The Cloud Storage bucket is empty

C. You did not write any SQL queries in the pipeline

D. The sink plugin is missing or not configured properly

Solution

1. Step 1: Check pipeline requirements

   Every Data Fusion pipeline needs a properly configured sink plugin to write data.

2. Step 2: Analyze failure reasons

   If the sink is missing or misconfigured, the pipeline cannot complete and will fail to start.

3. Final Answer:

   The sink plugin is missing or not configured properly -> Option D

4. Quick Check:

   Missing sink = pipeline start failure [OK]

Hint: Always configure sink plugin to start pipeline [OK]

Common Mistakes:

• Assuming empty bucket causes start failure
• Thinking SQL queries are mandatory
• Believing multiple sources cause failure

5. You want to build a Data Fusion pipeline that reads sales data from multiple Cloud Storage folders, cleans the data by removing duplicates, and writes the result to BigQuery daily. Which approach best fits this requirement?

hard

A. Use multiple source plugins for each folder, a Wrangler plugin to clean duplicates, and a BigQuery sink; schedule pipeline daily

B. Write a custom script outside Data Fusion to clean data, then use Data Fusion to load into BigQuery

C. Use a single source plugin pointing to one folder and manually merge data later

D. Create a pipeline with only a BigQuery source and sink without cleaning

Solution

1. Step 1: Identify data sources and cleaning needs

   Multiple folders require multiple source plugins or a pattern that reads all folders; cleaning duplicates needs a Wrangler or transform plugin.

2. Step 2: Design pipeline and scheduling

   Connect sources to cleaning plugin, then to BigQuery sink; schedule pipeline to run daily for automation.

3. Final Answer:

   Use multiple source plugins for each folder, a Wrangler plugin to clean duplicates, and a BigQuery sink; schedule pipeline daily -> Option A

4. Quick Check:

   Multiple sources + cleaning + sink + schedule = Use multiple source plugins for each folder, a Wrangler plugin to clean duplicates, and a BigQuery sink; schedule pipeline daily [OK]

Hint: Combine sources, cleaning plugin, sink, and schedule for ETL [OK]

Common Mistakes:

• Skipping cleaning step inside Data Fusion
• Using external scripts unnecessarily
• Ignoring scheduling for daily runs