To ensure that both production jobs with strict SLAs and ad-hoc queries have appropriate compute resources available while adhering to cost efficiency, setting up separate reservations and billing models for each project is the best approach. Here’s why option B is the best choice:

Separate Reservations for SLA and Ad-hoc Projects:

Creating two separate reservations allows for dedicated resource management tailored to the needs of each project.

The production project requires guaranteed slots with the ability to scale up as needed, while the ad-hoc project benefits from on-demand billing based on data scanned.

Enterprise Edition Reservation for SLA Project:

Setting a baseline of 300 slots ensures that the SLA project has the minimum required resources.

Enabling autoscaling up to 500 additional slots allows the project to handle occasional spikes in workload without compromising on SLAs.

On-Demand Billing for Ad-hoc Project:

Using on-demand billing for the ad-hoc project ensures cost efficiency, as users are billed based on the amount of data scanned rather than reserved slot capacity.

This model suits the less predictable and often lower-utilization nature of ad-hoc queries.

Steps to Implement:

Set Up Enterprise Edition Reservation for SLA Project:

Create a reservation with a baseline of 300 slots.

Enable autoscaling to allow up to an additional 500 slots as needed.

Configure On-Demand Billing for Ad-hoc Project:

Ensure that the ad-hoc project is set up to use on-demand billing, which charges based on data scanned by the queries.

Monitor and Adjust:

Continuously monitor the usage and performance of both projects to ensure that the configurations meet the needs and make adjustments as necessary.

Reference Links:

BigQuery Slot Reservations

BigQuery On-Demand Pricing

To retrieve all occurrences of street addresses from a BigQuery dataset, the most effective and comprehensive method is to use Cloud Data Loss Prevention (DLP). Here’s why option A is the best choice:

Cloud Data Loss Prevention (DLP):

Cloud DLP is designed to discover, classify, and protect sensitive information. It includes pre-defined infoTypes for various kinds of sensitive data, including street addresses.

Using Cloud DLP ensures thorough and accurate detection of street addresses based on advanced pattern recognition and contextual analysis.

Deep Inspection Job:

A deep inspection job allows you to scan entire tables for sensitive information.

By creating an inspection template that includes the STREET_ADDRESS infoType, you can ensure that all instances of street addresses are detected across your dataset.

Scalability and Accuracy:

Cloud DLP is scalable and can handle large datasets efficiently.

It provides a high level of accuracy in identifying sensitive data, reducing the risk of missing any occurrences.

Steps to Implement:

Set Up Cloud DLP:

Enable the Cloud DLP API in your Google Cloud project.

Create an Inspection Template:

Create an inspection template in Cloud DLP that includes the STREET_ADDRESS infoType.

Run Deep Inspection Jobs:

Create and run a deep inspection job for each table in your dataset using the inspection template.

Review the inspection job results to retrieve all occurrences of street addresses.

Reference Links:

Cloud DLP Documentation

Creating Inspection Jobs

Dataform assertions are data quality tests that find rows that violate one or more rules specified in the query. If the query returns any rows, the assertion fails. Dataform runs assertions every time it updates your SQL workflow and alerts you if any assertions fail. You can create assertions for all Dataform table types: tables, incremental tables, views, and materialized views. You can add built-in assertions to the config block of a table, such as nonNull and rowConditions, or create manual assertions with SQLX for advanced use cases. Dataform automatically creates views in BigQuery that contain the results of compiled assertion queries, which you can inspect to debug failing assertions. Dataform assertions are an efficient way to integrate data quality checks into your ELT solution in BigQuery by using Dataform. References: Test tables with assertions |Dataform | Google Cloud, Test data quality with assertions | Dataform, Data quality tests and documenting datasets | Dataform, Data quality testing with SQL assertions | Dataform

Cloud Bigtable is Google's NoSQL Big Data database service. It is the same database that Google uses for services, such as Search, Analytics, Maps, and Gmail.

It is used for requirements that are low latency and high throughput including Internet of Things (IoT), user analytics, and financial data analysis.

The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables. The Cloud Bigtable HBase client for Java makes it possible to use the HBase shell to connect to Cloud Bigtable.