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:
BigQuery Materialized Views
Optimizing Query Performance

A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.

If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.