How to normalize BigQuery data with a no-code recurring job?

PDE By islever Editorial Last updated

Short answer

A Use Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.

Question

You have data located in BigQuery that is used to generate reports for your company. You have noticed some weekly executive report fields do not correspond to format according to company standards. For example, report errors include different telephone formats and different country code identifiers. This is a frequent issue, so you need to create a recurring job to normalize the data. You want a quick solution that requires no coding. What should you do?
  1. Use Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job. Correct Answer
  2. Use Dataflow SQL to create a job that normalizes the data, and that after the first run of the job, schedule the pipeline to execute recurrently.
  3. Create a Spark job and submit it to Dataproc Serverless.
  4. Use BigQuery and GoogleSQL to normalize the data, and schedule recurring queries in BigQuery.

Key Takeaways

The exam tests your ability to match a 'no coding' requirement with a fully visual, code-free data transformation tool, while the common trap is choosing BigQuery scheduled queries because the data already resides in BigQuery.

For the Google Professional Data Engineer (PDE) exam, the correct answer is A: Use Cloud Data Fusion and Wrangler to normalize the data and schedule a recurring job. Community voting strongly favors A (81%), with many commenters citing Wrangler's no-code visual interface and scheduling capabilities.

Common mistake: The most common wrong answer is D: Use BigQuery and GoogleSQL to normalize the data and schedule recurring queries. This is tempting because the data is already in BigQuery and scheduled queries are simple, but SQL queries are still code, so it does not satisfy the explicit 'requires no coding' constraint.

Expert Analysis

Why the Answer Is Correct

Cloud Data Fusion Wrangler provides a no-code, visual interface for building data transformation pipelines. You can connect directly to BigQuery, use built-in transformations to normalize fields such as telephone numbers and country codes, and schedule the pipeline to run recurringly. Comment [1] reinforces this by stating "definitely A, cloud data fusion and wrangler to setup the clean up pipeline with no coding required," and comment [3] explains that Wrangler's no-code interface and Data Fusion's scheduling meet both requirements.

Why the Other Options Are Wrong

B (Dataflow SQL) requires writing or configuring SQL and then manually scheduling the pipeline after the first run, which is more complex and not a no-code solution. C (Dataproc Serverless with Spark) requires Spark code, clearly violating the no-coding requirement. D (BigQuery with GoogleSQL) is a strong distractor because the data is already in BigQuery, but GoogleSQL transformations such as REGEXP_REPLACE are code, not no-code. Comment [2] argues for D, but comment [4] incorrectly calls SQL "no-code," which misses the exam's intended distinction.

Community Comment Notes

The community overwhelmingly supports A, with 81% of votes and comments like [1], [3], [5], and [7]. Comment [5] provides the official Cloud Data Fusion documentation link, which is a reliable reference. A small minority (19%) chose D, often citing convenience since the data is already in BigQuery. However, the phrase "requires no coding" is the deciding factor, and Cloud Data Fusion Wrangler is the only option that is fully code-free and supports built-in recurring schedules.

Official Reference

https://cloud.google.com/data-fusion/docs

This link opens the official vendor documentation in a new tab.

Exam Strategy

When the question emphasizes 'no coding,' immediately eliminate any option that requires SQL, Spark, or other programming languages. Cloud Data Fusion Wrangler is the only fully visual, no-code tool with native scheduling, so identify it even when the data is already in BigQuery.

Community Votes

A
81%
D
19%

81% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 9 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 9

  1. Matt_108 👍 8 Answered A
    Definitely A, cloud data fusion and wrangler to setup the clean up pipeline with no coding required
  2. carmltekai 👍 2 Answered D
    The best solution here is D. Use BigQuery and GoogleSQL to normalize the data, and schedule recurring queries in BigQuery. Here's why: No-code solution: BigQuery's built-in capabilities and GoogleSQL offer a no-code way to transform and standardize data. You can leverage functions like REGEXP_REPLACE to normalize phone numbers and FORMAT to ensure consistent formatting across fields. Recurring jobs: BigQuery allows you to schedule queries to run regularly, which is perfect for maintaining data consistency over time. * Quick and efficient: BigQuery is designed for large-scale data processing, making it fast and efficient for normalization tasks.
  3. 987af6b 👍 2 Answered A
    A. Use Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job. Explanation No Coding Required: Cloud Data Fusion's Wrangler offers a no-code interface for data transformation tasks. You can visually design data normalization workflows without writing any code. Recurring Jobs: Cloud Data Fusion allows you to schedule these data normalization tasks to run on a recurring basis, meeting your need for automation.
  4. marlon.andrei 👍 2 Answered D
    The question say "You want a quick solution that requires no coding.". The data is in BQ, then is most easy normalize the data, and schedule recurring queries in BigQuery.
  5. scaenruy 👍 2 Answered A
    A. Use Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.
Show 4 more comments
  1. fitri001 👍 2 Answered A
    https://cloud.google.com/data-fusion/docs
  2. Sofiia98 👍 2 Answered A
    Cloud Data Fusion and Wrangler
  3. SohiniV 👍 1
    As per chatGPT, Option D allows you to utilize BigQuery's SQL capabilities to write queries that normalize the data according to company standards. You can then schedule these queries to run on a recurring basis using BigQuery's scheduled queries feature. This feature allows you to specify a schedule (e.g., weekly) for executing SQL queries automatically. This approach requires no additional setup or coding outside of BigQuery, making it a quick and straightforward solution to address the issue of data normalization.
  4. JyoGCP 👍 1 Answered A
    Option A

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