Using Cloud Composer to design the processing pipeline as a Directed Acyclic Graph (DAG) is the

most suitable approach because:

Fault tolerance: Cloud Composer (based on Apache Airflow) allows for handling failures at specific

stages. You can clear the state of a failed task and rerun it without reprocessing the entire pipeline.

Stage-based processing: DAGs are ideal for workflows with interdependent stages where the output

of one stage serves as input to the next.

Efficiency: This approach minimizes downtime and ensures that only failed stages are rerun, leading

to faster final output generation.

The storage system must balance cost, simplicity, and access patterns: high initial access, decreasing

over time, with some files remaining popular. Google Cloud Storage offers tailored options for this:

Option A: Custom Object Lifecycle Management (OLM) policies (e.g., transition to Nearline after 30

days, Archive after 90 days) are effective but static. They don’t adapt to actual usage, so popular old

files in Archive would incur high retrieval costs.

Option B: Autoclass automatically adjusts storage classes (Standard, Nearline, Coldline, Archive)

based on object access patterns, not just age. It keeps frequently accessed files in Standard (low

latency/cost for access) and moves inactive ones to cheaper classes, minimizing costs while

preserving simplicity. This fits the “some files remain popular” nuance.

Option C: A Cloud Scheduler job to manually change classes daily is complex (requires scripting,

monitoring), error-prone, and less cost-effective than automated solutions like Autoclass or OLM.

Option D: Defaulting to Archive is cheapest for storage but disastrous for access—retrieval costs and

latency would skyrocket for initial high-access periods.

Why B is Best: Autoclass simplifies management (no rules to define) and optimizes costs dynamically.

For videos, where access varies unpredictably, it ensures popular files stay accessible without manual

intervention, aligning with Google’s cost-optimization guidance.

Extract from Google Documentation: From "Autoclass in Cloud Storage"

(https://cloud.google.com/storage/docs/autoclass): "Autoclass automatically transitions objects to

the most cost-effective storage class based on access patterns, simplifying management and

reducing costs for workloads with variable access, such as media files."

Reference: Google Cloud Documentation - "Cloud Storage Autoclass"

(https://cloud.google.com/storage/docs/autoclass).

:

Using BigQuery ML is the best solution in this case because:

Ease of use: BigQuery ML allows users to build machine learning models using SQL, which requires

minimal ML expertise.

Integrated platform: Since the data already exists in BigQuery, there’s no need to move it to another

service, saving time and engineering resources.

Logistic regression: This is an appropriate model for binary classification tasks like predicting the

likelihood of a customer making a purchase in the next month.

Comprehensive and Detailed In-Depth

BigQuery uses IAM for access control, adhering to least privilege by granting only necessary

permissions.

Option A: IAM roles (e.g., roles/bigquery.dataViewer for read-only) restrict query access to

authorized users, aligning with Google’s security best practices.

Option B: BigQuery doesn’t support SQL GRANT for dataset privileges; access is managed via IAM or

authorized views.

Option C: Exporting to Cloud Storage with signed URLs bypasses BigQuery’s native controls and adds

complexity.

Option D: CMEK encrypts data at rest but doesn’t control query access.

Extract from Google Documentation: From "Controlling Access to BigQuery"

(https://cloud.google.com/bigquery/docs/access-control): "Use IAM to manage access to BigQuery

resources. Predefined roles like roles/bigquery.dataViewer allow you to grant query access while

following the principle of least privilege."

Reference: Google Cloud Documentation - "BigQuery IAM"

(https://cloud.google.com/bigquery/docs/access-control).

Using the "Pub/Sub to BigQuery" Dataflow template with a UDF (User-Defined Function) is the

optimal choice because it combines near real-time processing, minimal code for transformations,

and scalability. The UDF allows for efficient implementation of custom transformations, such as

capitalizing letters in the serial number field, while Dataflow handles the rest of the managed

pipeline seamlessly.

Since your existing data pipeline tools already support connectors to BigQuery, the most efficient

approach is to use the existing data pipeline tool's BigQuery connector to reconfigure the data

mapping. This leverages your current tools, reducing migration complexity and setup time, while

optimizing migration speed. By reconfiguring the data mapping within the existing pipeline, you can

seamlessly direct the data into BigQuery without needing additional services or intermediary steps.

Using customer-managed encryption keys (CMEK) allows you to encrypt highly sensitive data in

BigQuery with encryption keys managed by your organization. When an employee leaves the

organization, you can render the data unreadable by deleting or revoking access to the encryption

keys associated with the data. This approach ensures compliance with strict data privacy regulations

by making the data inaccessible without the encryption keys, providing strong control over data

access and security.

Using Analytics Hub to create a private exchange with data egress restrictions ensures controlled

sharing of the dataset while minimizing the risk of unauthorized copying. This approach allows you to

provide secure, managed access to the dataset without giving direct access to the raw data. The

egress restriction ensures that data cannot be exported or copied outside the designated boundaries.

Additionally, this solution provides a reusable framework that simplifies future data sharing with

other teams or projects while maintaining strict data governance.

Extract from Google Documentation: From "Analytics Hub Overview"

(https://cloud.google.com/analytics-hub/docs): "Analytics Hub enables secure, controlled data

sharing with private exchanges. Combine with organization policies like restrictDataEgress to prevent

data copying, providing a reusable framework for sharing BigQuery datasets across teams."

Reference: Google Cloud Documentation - "Analytics Hub" (https://cloud.google.com/analytics-hub).

Comprehensive and Detailed In-Depth

For a 25 TB dataset, efficiency and cost require minimizing data movement and leveraging

BigQuery’s scalability within Colab Enterprise.

Option A: Exporting 25 TB to Google Drive and loading via Pandas is impractical (size limits, transfer

costs) and slow.

Option B: BigQuery magic commands (%%bigquery) in Colab Enterprise allow direct querying of

BigQuery data, keeping processing in the cloud, reducing costs, and enabling collaboration.

Option C: Dataproc with Spark adds cluster costs and complexity, unnecessary when BigQuery can

handle the workload.

Option D: Copying 25 TB to local storage is infeasible due to size and cost.

Extract from Google Documentation: From "Using BigQuery with Colab Enterprise"

(https://cloud.google.com/colab/docs/bigquery): "You can use BigQuery magic commands

(%%bigquery) in Colab Enterprise to execute SQL queries directly against BigQuery datasets,

providing efficient access to large-scale data without moving it."

Reference: Google Cloud Documentation - "Colab Enterprise with BigQuery"

(https://cloud.google.com/colab/docs).