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To ensure that resources are limited to only the europe-west3 region, you should set the organization policy constraint constraints/gcp.resourceLocations to in:europe-west3-locations. This policy restricts the deployment of resources to the specified locations, which in this case is the europe-west3 region. By setting this policy, you enforce location compliance across your Google Cloud resources, aligning with the best practices for data governance and regulatory compliance.

References:

Professional Data Engineer Certification Exam Guide | Learn - Google Cloud1.

Preparing for Google Cloud Certification: Cloud Data Engineer2.

Professional Data Engineer Certification | Learn | Google Cloud3.

3: Professional Data Engineer Certification | Learn | Google Cloud 2: Preparing for Google Cloud Certification: Cloud Data Engineer 1: Professional Data Engineer Certification Exam Guide | Learn - Google Cloud

To optimize the performance of a complex Spark job on Dataproc that heavily relies on shuffling operations, and given the cost constraints of using preemptible VMs, switching from HDDs to SSDs and using HDFS as an intermediate storage layer can significantly improve performance. Here’s why option C is the best choice:

Performance of SSDs:

SSDs provide much faster read and write speeds compared to HDDs, which is crucial for performance-intensive operations like shuffling in Spark jobs.

Using SSDs can reduce I/O bottlenecks during the shuffle phase of your Spark job, improving overall job performance.

Intermediate Storage with HDFS:

Copying data from Google Cloud Storage (GCS) to HDFS for intermediate storage can reduce latency compared to reading directly from GCS.

HDFS provides better locality and faster data access within the Dataproc cluster, which can significantly improve the efficiency of shuffling and other I/O operations.

Cost Considerations:

Although SSDs are more expensive than HDDs, the performance improvement for shuffle-heavy workloads can justify the cost, especially if the improved performance reduces the overall runtime and thereby the cost of using preemptible VMs.

Using preemptible VMs with SSDs for this workload balances the cost and performance trade-offs effectively.

To improve the performance of querying a large BigQuery table with filters on countryname and username, clustering the table by these fields is the most effective approach. Here’s why option C is the best choice:

Clustering in BigQuery:

Clustering organizes data based on the values in specified columns. This can significantly improve query performance by reducing the amount of data scanned during query execution.

Clustering by countryname and username means that data is physically sorted and stored together based on these fields, allowing BigQuery to quickly locate and read only the relevant data for queries using these filters.

Filter Efficiency:

With the table clustered by countryname and username, queries that filter on these columns can benefit from efficient data retrieval, reducing the amount of data processed and speeding up query execution.

This directly addresses the performance issue of the dashboard queries that apply filters on these fields.

Steps to Implement:

Redesign the Table:

Create a new table with clustering on countryname and username:

CREATE TABLE project.dataset.new_table

CLUSTER BY countryname, username AS

SELECT * FROM project.dataset.customer_order;

Migrate Data:

Transfer the existing data from the original table to the new clustered table.

Update Queries:

Modify the dashboard queries to reference the new clustered table.

Reference Links:

BigQuery Clustering Documentation

Optimizing Query Performance

To ensure that all employees can search and find tables with GDPR tags while restricting data access to sensitive tables only to the HR group, follow these steps:

Data Catalog Tag Template:

Use Data Catalog to create a tag template named "gdpr" with a boolean field "has sensitive data". Set the visibility to public so all employees can see the tags.

Roles and Permissions:

Assign the datacatalog.tagTemplateViewer role to the all employees group. This role allows users to view the tags and search for tables based on the "has sensitive data" field.

Assign the bigquery.dataViewer role to the HR group specifically on tables that contain sensitive data. This ensures only HR can access the actual data in these tables.

Steps to Implement:

Create the GDPR Tag Template:

Define the tag template in Data Catalog with the necessary fields and set visibility to public.

Assign Roles:

Grant the datacatalog.tagTemplateViewer role to the all employees group for visibility into the tags.

Grant the bigquery.dataViewer role to the HR group on tables marked as having sensitive data.

Reference Links:

Data Catalog Documentation

Managing Access Control in BigQuery

IAM Roles in Data Catalog