Exam Professional Data Engineer topic 1 question 30 discussion

It is a GOOGLE exam. The answer won't be on-premise or OLAP cubes even if it is the easiest. The answer is B

Answer: D Description: Easy and it won’t affect processing

I think it's B, today BigQuery has multiple connectors that can allow an easy connection to external data sources without impacting the database itself, even if the database was in a SQL instance, MySQL, Federated queries could be used. In my opinion it's B

Since the question mentions the nightly backup, why we cannot use it? ETL reduces the impact of the source system but still some impacts. D doesn't add any additional impact.

Believe me all questions were from Exam topic all were there yesterday in exam. But yes dont go with starting questions mainly focus questions after 200 and latest questions are at last page.

Answer B: I don't know why people are choosing D. It is two steps, first cloudsql and then dataproc, a lot of overhead. BigQuery is just perfect fit.

This won’t affect processing

Based on these considerations, option B is likely the best approach. By using an ETL tool to load data from MySQL into Google BigQuery, you're leveraging BigQuery's strengths in handling large-scale analytics workloads without impacting the performance of the operational databases. This option provides a clear separation of operational and analytical workloads and takes advantage of BigQuery's fast analytics capabilities.

Do not spend much time on in - just B

Answer is B - Use an ETL tool to load the data from MySQL into Google BigQuery. * Google BigQuery is a serverless, highly scalable data warehouse that can handle large-scale analytics workloads without impacting your MySQL cluster's performance. * Using an ETL (Extract, Transform, Load) tool to transfer data from MySQL to BigQuery allows you to maintain a separate analytics environment, ensuring that your operational database remains unaffected. Option C (connecting an on-premises Apache Hadoop cluster to MySQL and performing ETL) introduces complexity and may not be as scalable as a cloud-based solution. Option D (mounting backups to Google Cloud SQL and processing the data using Google Cloud Dataproc) could be an option for historical data analysis but might not be the best choice for real-time analytics while the MySQL cluster is under heavy load. Additionally, the backups need to be restored and processed, which might introduce some delay.

It's GOOGLE exam where choosing the GCP service shall be first preference. Now notice the problem statement "perform analytics with minimal impact on operations" BigQuery is right option for analytic as well as Cloud SQL does provide easy export to GCS where we can query from BigQuery without loading into BQ to save storage cost.

B. Use an ETL tool to load the data from MySQL into Google BigQuery. * Google BigQuery is a serverless, highly scalable data warehouse that can handle large-scale analytics workloads without impacting your MySQL cluster's performance. * Using an ETL (Extract, Transform, Load) tool to transfer data from MySQL to BigQuery allows you to maintain a separate analytics environment, ensuring that your operational database remains unaffected. Option C (connecting an on-premises Apache Hadoop cluster to MySQL and performing ETL) introduces complexity and may not be as scalable as a cloud-based solution. Option D (mounting backups to Google Cloud SQL and processing the data using Google Cloud Dataproc) could be an option for historical data analysis but might not be the best choice for real-time analytics while the MySQL cluster is under heavy load. Additionally, the backups need to be restored and processed, which might introduce some delay.

The question clearly says there is load on MYSQL already so doing analytics on it is bad idea. Its bad to run analytics on MYSQL but still a better option to run etl with it to load it to BigQuery.

B. Use an ETL tool to load the data from MySQL into Google BigQuery. This way, analytics is entirely separated from the operational database, and BigQuery is well-suited for large-scale analytics.

The correct answer is to build a read replica :-) but since we can't do that then migrating to BigQuery will have to suffice.

Answer is Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc. A: OLAP on MySQL performs poorly. B: ETL consumes lot of MySQL resources, to read the data, as per question MySQL is under pressure already. C: Similar to B. D: By mounting backup can avoid reading from MySQL, data freshness is not an issue as per the question (and is not mention in the question).

Wouldn't the correct answer be to create read replica and do analytics off of that?