Exam Professional Data Engineer topic 1 question 47 discussion

You want to optimize the data schema + Machine Learning --> Bigquery. So A

Answer: Should be D - Datastore

The answer should be Cloud SQL because it is a relational database suitable for transactional data. BigQuery is for analytics and querying - not suitable for transactional workload. Bigtable is for unstructured and time series data. Datastore is a nosql document database for semi structured data.

For a machine learning-based food ordering service that requires optimised storage of transactional data, Google Cloud BigQuery is a suitable choice

ithin Google Cloud, the database that most readily allows for data schema optimization is BigQuery; it provides features like schema auto-detection, columnar storage, and the ability to manually define your schema to tailor it for efficient querying and analysis of large datasets.

Cloud SQL is the best choice for your application as it provides relational database management and is optimized for storing transactional data with SQL querying capabilities. It is well-suited for managing user profiles, account information, and orders, ensuring data integrity, and supporting complex queries necessary for the food ordering service.

Use BigQuery for analyzing aggregated data (e.g., predicting food trends or training ML models). Use Cloud Bigtable for large-scale real-time recommendation engines if needed in the future. Use Firestore for dynamic, semi-structured data with real-time updates if you need flexibility over transactional consistency. Cloud SQL strikes the right balance for this use case due to its support for structured data, transactions, and easy integration with other GCP services. So B. CloudSQL is the right answer

Cloud SQL can store transactional data not Big Query. Big Query is an analytical service.

Easy implement data schema + Machine Learning model in Big Query

database will be used to storage all transactional data.... I think that you need a relational database for that, then federated tables to bigquery to analysis.

For a machine learning-based food ordering service that needs to store transactional data, Cloud SQL is the most suitable option. Here's why: Cloud SQL is a fully-managed relational database service that supports transactional workloads, making it ideal for storing user profiles, account information, and order details. It provides strong consistency and supports complex queries, which are essential for managing and retrieving transactional data efficiently. While BigQuery is excellent for large-scale data analysis, it is not optimized for transactional data storage. Cloud Bigtable is designed for high-throughput and low-latency workloads but lacks the transactional capabilities needed for this use case. Cloud Datastore is a NoSQL database that supports transactions but is generally less powerful than a relational database for complex transactional schemas12. So, the best choice for your needs is B. Cloud SQL.

No idea why so many people go to A. But as transactional data, I think B is correct.

The details of the information definitely look suited to noSql to me, so that means C or D. Datastore is designed for this sort of thing - transactional nosql for an App. I took the question to mean "the machine learning app already exists" so the fact bigquery allows ML isn't relevant. It would be a leap to assume that the ML is done in Bigquery (I have a current Google ML pro cert, and this wouldn't say bigquery to me from that cert)

Cloud SQL is a fully-managed relational database service that supports MySQL, PostgreSQL, and SQL Server. It is well-suited for transactional workloads, allowing you to store structured data with relationships between different entities, such as users, orders, and profiles.

he best answer for this scenario is B. Cloud SQL. Here's why: Relational Data: The information you need to store (user profile, account information, order information) is highly structured and relational. Cloud SQL, being a relational database service, is designed to handle this type of data efficiently. Transactional Workloads: Food ordering involves transactional operations (placing orders, updating user preferences, etc.). Cloud SQL is optimized for transactional workloads, ensuring data consistency and integrity. Ease of Use: Cloud SQL is a managed service, meaning Google handles maintenance, updates, and backups, making it easier to manage than some other options. Integration with Machine Learning: Cloud SQL can easily integrate with other Google Cloud Platform products like BigQuery and Vertex AI, which are crucial for machine learning tasks.

BigQuery is a fully managed, serverless data warehouse that enables scalable analysis of large datasets. It is designed to handle large volumes of data and support complex queries, making it suitable for storing transactional data and performing analytics. With BigQuery, you can optimize your data schema and easily scale as your data grows. Additionally, BigQuery integrates well with other Google Cloud Platform services, including machine learning services, enabling you to build advanced analytics and predictive models on your transactional data.

C It says that the database will be used to store the transactions data. BigQuery is not usually characterized as a data storage system. Also a databased is used for storing transactional Data not a Data Wharehouse.