Exam Professional Data Engineer topic 1 question 225 discussion

Both Spark and BigQuery can directly access data in Cloud Storage.

The question states that the data should be available on BigQuery and only option C meets this requirement.

A is correct

Option B: Registering the bucket as a Dataplex asset adds an additional layer of data governance and management. While useful, it may not be necessary for your immediate migration needs and can introduce additional complexity. Option C: Migrating data directly to BigQuery would require significant changes to your Spark pipelines since they would need to be refactored to read from and write to BigQuery instead of Parquet files. This approach could introduce higher costs due to BigQuery storage and querying. Option D: Using BigLake and Dataproc on Compute Engine is more complex and requires more management compared to Dataproc Serverless. Additionally, it might not be as cost-effective as leveraging Cloud Storage and Dataproc Serverless.

The question says "You want to use managed services, while minimizing ETL data processing changes and overhead costs". Dataproc is a managed service that doesn't need to refactor the data transformation Spark code you already have (you will have to refactor only the wrtie and read code), an it has a Big Query connector for future use. https://cloud.google.com/dataproc/docs/concepts/connectors/bigquery

Migrate your data directly to BigQuery. Refactor Spark pipelines to read from and write to BigQuery. Run the Spark jobs on Dataproc Serverless. The best choice for ensuring data availability in BigQuery. It allows seamless integration with BigQuery and minimizes ETL changes.

A tricky one, because of "you need to ensure that your data is available in BigQuery". The easiest and most straight forward migration seems answer A to me, and then you can use external tables to make the parquet data directly available in BigQuery. https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-parquet However creating the external tables is an extra step? So therefore maybe C is the answer?

I think the key phrase here is "you need to ensure that your data is available in BigQuery" that's why I thing C it's the best option

I think it's C. Dataproc can use BigQuery to read and write data. Dataproc's BigQuery connector is a library that allows Spark and Hadoop applications to process and write data from BigQuery. Here's how Dataproc can be used with BigQuery: Process large datasets: Use Spark to process data stored in BigQuery. Write results: Write the results back to BigQuery or other data storage for further analysis. Read data: The BigQuery connector can read data from BigQuery into a Spark DataFrame. Write data: The connector writes data to BigQuery by buffering all the data into a Cloud Storage temporary table.

I think its B and the reason is that egistering the data as a Dataplex asset enables seamless integration with BigQuery later on. Dataplex simplifies data discovery and lineage tracking, making it easier to prepare your data for BigQuery transformations.

Why would I select A here? Why not moving the data to BigQuery and running Dataproc Serverless jobs accessing the data in BigQuery?

- This option involves moving Parquet files to Cloud Storage, which is a common and cost-effective storage solution for big data and is compatible with Spark jobs. - Using Dataproc Metastore to manage metadata allows us to keep Hadoop ecosystem's structural information. - Running Spark jobs on Dataproc Serverless takes advantage of managed Spark services without managing clusters. - Once the data is in Cloud Storage, you can also easily load it into BigQuery for further analysis.

Managed Services: Leverages Dataproc Serverless for a fully managed Spark environment, reducing overhead and administrative tasks. Minimal Data Processing Changes: Keeps Spark pipelines largely intact by working with Parquet files on Cloud Storage, minimizing refactoring efforts. BigQuery Integration: Dataproc Serverless can directly access BigQuery, enabling future transformation pipelines without additional data movement. Cost-Effective: Serverless model scales resources only when needed, optimizing costs for intermittent workloads.