To improve response times and reduce costs for frequent queries aggregating a large sales history fact table, materialized views are a highly effective solution. Here's why option A is the best choice:
Materialized Views:
Materialized views store the results of a query physically and update them periodically, offering faster query responses for frequently accessed data.
They are designed to improve performance for repetitive and expensive aggregation queries by precomputing the results.
Efficiency and Cost Reduction:
By building materialized views at the day and month level, you significantly reduce the computation required for each query, leading to faster response times and lower query costs.
Materialized views also reduce the need for on-demand query execution, which can be costly when dealing with large datasets.
Minimized Maintenance:
Materialized views in BigQuery are managed automatically, with updates handled by the system, reducing the maintenance burden on your team.
Steps to Implement:
Identify Aggregation Queries:
Analyze the existing queries to identify common aggregation patterns at the day and month levels.
Create Materialized Views:
Create materialized views in BigQuery for the identified aggregation patterns. For example CREATE MATERIALIZED VIEW project.dataset.sales_daily_summary AS SELECT DATE(transaction_time) AS day, SUM(amount) AS total_sales FROM project.dataset.sales GROUP BY day; CREATE MATERIALIZED VIEW project.dataset.sales_monthly_summary AS SELECT EXTRACT(YEAR FROM transaction_time) AS year, EXTRACT(MONTH FROM transaction_time) AS month, SUM(amount) AS total_sales FROM project.dataset.sales GROUP BY year, month; Query Using Materialized Views:
Update existing queries to use the materialized views instead of directly querying the base table.
Reference Links:
BigQuery Materialized Views
Optimizing Query Performance

The key requirements are:
Migrate Spark 3 batch job.
Minimally change the job (reads from GCS, writes to BQ - standard for Spark on GCP).
Job optimized for Spark (specific executor vCPU/memory).
Ability to choose similar executor settings.
Minimize installation and management effort.
Dataproc Serverless (Option A)is designed for these use cases.
Spark 3 Support:Dataproc Serverless supports various Spark runtimes, including Spark 3.
Minimal Changes:Spark jobs reading from GCS and writing to BigQuery (using the Spark-BigQuery connector) are standard. Minimal code changes are generally needed.
Customizable Resources:Dataproc Serverless allows you to specify resources for the driver and executors, including vCPU and memory. You can configure these to match your optimized on-premises settings (e.g., 8 vCPU, 16 GB memory per executor, though specific available configurations should be checked).
Minimal Installation and Management:This is the core benefit of "serverless." You don't need to provision, manage, or scale clusters. You submit your batch job, and Google Cloud handles the underlying infrastructure. This significantly reduces operational overhead.
Let's analyze why other options are less suitable:
B (Compute Engine VM):You would need to manually install Spark, configure it, manage dependencies, and manage the VM itself. This is high management effort.
C (Google Kubernetes Engine cluster):While you can run Spark on GKE (e.g., using Spark on Kubernetes operator), it involves managing the GKE cluster, Spark deployment configurations, Docker images, etc. This is also significant management effort, more than Dataproc Serverless.
D (Dataproc cluster):A standard Dataproc cluster provides more control than serverless but also requires more management (creating, scaling, and managing the cluster lifecycle). Dataproc Serverless is specifically designed to minimize this management for batch jobs. Given the "minimize installation and management effort" requirement, serverless is preferred over a managed cluster if it meets the job's needs.
Reference:
Google Cloud Documentation: Dataproc Serverless > Overview. "Dataproc Serverless for Spark lets you run Spark batch workloads without requiring you to provision and manage your own cluster... Submit your Spark workload to the Dataproc Serverless service. The service will run the workload on a managed compute infrastructure, autoscaling resources as needed." Google Cloud Documentation: Dataproc Serverless > Submitting Spark batch workloads > Spark batch workload properties. This documentation shows how you can specify properties for driver and executor cores (spark.driver.cores, spark.executor.cores) and memory (spark.driver.memory, spark.executor.memory), allowing you to choose settings similar to your existing optimized job.

BigQuery Omni is a multi-cloud analytics solution that lets you use the BigQuery interface to analyze data stored in other public clouds, such as AWS and Azure, without moving or copying the data. BigLake tables are a type of external table that let you query structured data in external data stores with access delegation. By using BigQuery Omni and BigLake tables, you can query data in AWS and Azure object stores directly from BigQuery, with minimal data movement and consistent performance. References:
* 1: Introduction to BigLake tables
* 2: Deep dive on how BigLake accelerates query performance
* 3: BigQuery Omni and BigLake (Analytics Data Federation on GCP)