Creatingmaterialized viewsis the best solution to improve dashboard loading time while ensuring that the data is as up-to-date as possible. Materialized views precompute and cache the results of complex joins and aggregations, significantly reducing query execution time for dashboards. They also automatically update as the underlying data changes, ensuring near real-time access to fresh data. This approach optimizes query performance and provides an efficient and scalable solution for streaming data dashboards.

For near real-time dashboards with slow queries involving joins and aggregations, Google recommends materialized views in BigQuery to precompute and refresh results automatically.

Option A: Scheduled queries compute intermediate results periodically, but they aren’t near real-time and require manual scheduling, adding latency.

Option B: Parameterized data types don’t address query performance for joins/aggregations; they’re for query flexibility, not optimization.

Option C: Disabling caching worsens performance by forcing full recomputation each time, contrary to the goal.

roles/bigquery.jobUser:

This role allows a user or service account to run BigQuery jobs, including queries. This is necessary for the application to interact with and query the tables.

From Google Cloud documentation: "BigQuery Job User can run BigQuery jobs, including queries, load jobs, export jobs, and copy jobs."

roles/bigquery.dataOwner:

This role grants full control over BigQuery datasets and tables. It allows the service account to update tables, which is a requirement of the application.

From Google Cloud documentation: "BigQuery Data Owner can create, delete, and modify BigQuery datasets and tables. BigQuery Data Owner can also view data and run queries."

Why other options are incorrect:

B. roles/bigquery.connectionUser and roles/bigquery.dataViewer:

roles/bigquery.connectionUser is used for external connections, which is not required for this task. roles/bigquery.dataViewer only allows viewing data, not updating it.

C. roles/bigquery.admin:

roles/bigquery.admin grants excessive permissions. Following the principle of least privilege, this role is too broad.

D. roles/bigquery.user and roles/bigquery.filteredDataViewer:

roles/bigquery.user grants the ability to run queries, but not the ability to modify data. roles/bigquery.filteredDataViewer only provides permission to view filtered data, which is not sufficient for updating tables.

Principle of Least Privilege:

The principle of least privilege is a security concept that states that a user or service account should be granted only the permissions necessary to perform its intended tasks.

By assigning roles/bigquery.jobUser and roles/bigquery.dataOwner, we provide the application with the exact permissions it needs without granting unnecessary access.

Google Cloud Documentation References:

BigQuery IAM roles:https://cloud.google.com/bigquery/docs/access-control-basic-roles

IAM best practices:https://cloud.google.com/iam/docs/best-practices-for-using-iam

Creating amaterialized view in BigQuerywith theSUM()function and theDATE_SUB()function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using theDATE_SUB()function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.

Comprehensive and Detailed In-Depth Explanation:

Transferring 300 TB over a 100 Mbps connection would take an impractical amount of time (over 300 days at theoretical maximum speed, ignoring real-world constraints like latency). Google Cloud provides the Transfer Appliance for large-scale, time-sensitive transfers.

Option A: Cloud Client Libraries over the internet would be slow and unreliable for 300 TB due to bandwidth limitations.

Option B: The gcloud storage command is similarly constrained by internet speed and not designed for such large transfers.

Option C: Compressing and splitting across multiple providers adds complexity and isn’t a Google-supported method for Cloud Storage ingestion.

Comprehensive and Detailed In-Depth Explanation:

The strategy requires storage in two specific regions with automated high availability (HA). Cloud Storage location options dictate the solution:

Option A: A dual-region bucket (e.g., us-west1 and us-east1) replicates data synchronously across two user-specified regions, ensuring HA without manual intervention. It’s fully automated and meets the requirement.

Option B: Two single-region buckets with gcloud storage replication is manual, not automated, and lacks real-time HA (requires scripting and monitoring).

Option C: Multi-region buckets (e.g., us) span multiple regions within a geography but don’t let you specify exactly two regions, potentially violating the restriction.

To build a serverless data pipeline that processes data in real-time from Pub/Sub, transforms it, and stores it for SQL-based analysis using Looker, the best solution is to useDataflowandBigQuery.Dataflowis a fully managed service for real-time data processing and transformation, whileBigQueryis a serverless data warehouse that supports SQL-based querying and integrates seamlessly with Looker for data analysis and visualization. This combination meets the requirements for real-time streaming, transformation, and efficient storage for analytical queries.

Migrating the Spark jobs toDataproc Serverlessis the best approach because it allows you to run Spark workloads without the need to provision or manage clusters. Dataproc Serverless automatically scales resources based on workload requirements, simplifying operations and reducing administrative overhead. This solution is ideal for organizations that want to focus on managing their Spark code without worrying about the underlying infrastructure. It is cost-effective and fully managed, aligning well with the goal of minimizing cluster management.