Exam Professional Data Engineer topic 1 question 269 discussion

Data Catalog is the best choice. But for catalogging PostgreSQL it is better to use a connector when available, instead of using API. https://cloud.google.com/data-catalog/docs/integrate-data-sources#integrate_unsupported_data_sources

- It utilizes Data Catalog's native support for both BigQuery datasets and Pub/Sub topics. - For PostgreSQL tables running on a Compute Engine instance, you'd use Data Catalog APIs to create custom entries, as Data Catalog does not automatically discover external databases like PostgreSQL.

This is C. To clarify some issues below with B, the links provided by supporters of B actually do say that it's preferable to use a community connector where available, and to only use the API when the case is genuinely not supported by community connectors. In this case it's Postgresql, so it's supported, see here for full list: https://cloud.google.com/data-catalog/docs/integrate-data-sources#integrate_on-premises_data_sources So this would be B if it was something like Q+ or some genuinely unsupported database, but postgres is supported for community connector.

Data Catalog automatically catalogs metadata from Google Cloud sources such as BigQuery, Vertex AI, Pub/Sub, Spanner, Bigtable, and more. To catalog metadata from non-Google Cloud systems in your organization, you can use the following: Community-contributed connectors to multiple popular on-premises data sources Manually build on the Data Catalog APIs for custom entries

raaad mostly correct and we can check his description supporting his answer so we can go with it .Cheers mate

I’m voting for C because the documentation states that Postgres is a custom connector developed by the community.

BigQuery Datasets and Pub/Sub Topics: Google Data Catalog can automatically catalog metadata from BigQuery and Pub/Sub, making it easy to discover and manage these data assets without additional development effort. PostgreSQL Tables: While Data Catalog does not have built-in connectors for PostgreSQL, you can use the Data Catalog APIs to manually catalog the PostgreSQL tables. This requires some custom development but is manageable compared to creating custom connectors for everything.

B. Use Data Catalog to automatically catalog BigQuery datasets and Pub/Sub topics. Use Data Catalog APIs to manually catalog PostgreSQL tables.

Option B leverages Data Catalog to automatically catalog BigQuery datasets and Pub/Sub topics, which streamlines the process and reduces manual effort. Using Data Catalog APIs to manually catalog PostgreSQL tables ensures consistency across all data assets while minimizing development and configuration efforts.

I vote for c as per Integrate on-premises data sources To integrate on-premises data sources, you can use the corresponding Python connectors contributed by the community: under the link https://cloud.google.com/data-catalog/docs/integrate-data-sources

In the opction C, the expression "Use custom connectors to manually catalog PostgreSQL tables." is refering to the use case of Google when you want to use "Community-contributed connectors to multiple popular on-premises data sources". As you can see, this connectors are for ON-PREMISSES data sources ONLY. In this case the Postgres is in a VM in the cloud. Thus, the option correct is B.

option B, there's no need to build a custom connector now, postgreSQL is now supported https://github.com/GoogleCloudPlatform/datacatalog-connectors-rdbms/tree/master/google-datacatalog-postgresql-connector

Use Data Catalog to automatically catalog BigQuery datasets and Pub/Sub topics. Use Data Catalog APIs to manually catalog PostgreSQL tables.

Datacatalog API contain the connector for postgresql with using it developer don't have to create the custom connectors

Google Recommendation: If you can't find a connector for your data source, you can still manually integrate it by creating entry groups and custom entries. To do that, you can: - Use one of the Data Catalog Client Libraries in one of the following languages: C#, Go, Java, Node.js, PHP, Python, or Ruby. - Or manually build on the Data Catalog API. However, there is a connector for PostgreSQL, so option C.

If you can't find a connector for your data source, you can still manually integrate it by creating entry groups and custom entries. To do that, you can: - Manually build on the Data Catalog API.

Option B - Data Catalog automatically maps out GCP resources and dev efforts are minimized by leveraging the data catalog API to do the same for postgresql db