The key requirements are:

Data on Cloud Storage (migrated from HDFS).

Processing with Spark and SQL.

Column-level security.

Cost-effective and scalable for a data mesh.

Let's analyze the options:

Option A (Load to BigQuery tables, policy tags, Spark-BQ connector/BQ SQL):

Pros: BigQuery native tables offer excellent performance. Policy tags provide robust column-level security managed centrally in Data Catalog. The Spark-BigQuery connector allows Spark to read from/write to BigQuery. BigQuery SQL is powerful. Scales well.

Cons: "Loading" the data into BigQuery means moving it from Cloud Storage into BigQuery's managed storage. This incurs storage costs in BigQuery and an ETL step. While effective, it might not be the most "cost-effective" if the goal is to query data in place on Cloud Storage, especially for very large datasets.

Option B (Long-living Dataproc, Hive, Ranger):

Pros: Provides a Hadoop-like environment with Spark, Hive, and Ranger for column-level security.

Cons: "Long-living Dataproc cluster" is generally not the most cost-effective, as you pay for the cluster even when idle. Managing Hive and Ranger adds operational overhead. While scalable, it requires more infrastructure management than serverless options.

Option C (IAM at file level, BQ external table, Dataproc Spark):

Pros: Using Cloud Storage is cost-effective for storage. BigQuery external tables allow SQL access.

Cons: IAM at the file level in Cloud Storage does not provide column-level security. This option fails to meet a critical requirement.

Option D (Define a BigLake table, policy tags, Spark-BQ connector/BQ SQL):

Pros:BigLake Tables: These tables allow you to query data in open formats (like Parquet, ORC) on Cloud Storage as if it were a native BigQuery table, but without ingesting the data into BigQuery's managed storage. This is highly cost-effective for storage.

Column-Level Security with Policy Tags: BigLake tables integrate with Data Catalog policy tags to enforce fine-grained column-level security on the data residing in Cloud Storage. This is a centralized and robust security model.

Spark and SQL Access: Data scientists can use BigQuery SQL directly on BigLake tables. The Spark-BigQuery connector can also be used to access BigLake tables, enabling Spark processing.

Cost-Effective & Scalable Data Mesh: This approach leverages the cost-effectiveness of Cloud Storage, the serverless querying power and security features of BigQuery/Data Catalog, and provides a clear path to building a data mesh by allowing different domains to manage their data in Cloud Storage while exposing it securely through BigLake.

Cons: Performance for BigLake tables might be slightly different than BigQuery native storage for some workloads, but it's designed for high performance on open formats.

Why D is superior for this scenario:

BigLake tables (Option D) directly address the need to keep data in Cloud Storage (cost-effective for a data lake) while providing strong, centrally managed column-level security via policy tags and enabling both SQL (BigQuery) and Spark (via Spark-BigQuery connector) access. This is more aligned with modern data lakehouse and data mesh architectures than loading everything into native BigQuery storage (Option A) if the data is already in open formats on Cloud Storage, or managing a full Hadoop stack on Dataproc (Option B).