The most critical requirement is to combine the price data with other BigQuery data "as cheaply as possible." This refers primarily to query costs, which dominate overall costs in analytical workloads. Loading the small (100-row) price dataset into a native BigQuery table is the most performant and cost-effective solution for the join operations. Batch loads into BigQuery are free, and the storage cost for such a small table is negligible. By having the data as a native table, BigQuery's query engine can perform highly optimized, fast, and inexpensive joins against the large customer datasets. The other options introduce significant performance overhead or unnecessary complexity and cost.

B: Federated queries on Cloud Storage are generally slower and less efficient than queries against native BigQuery tables, especially for frequent join operations, which would increase overall query costs and time.

C: Using Cloud Dataflow to programmatically join data from BigQuery and Cloud Datastore is an expensive anti-pattern for this use case. It introduces high latency and significant processing costs for a task that a simple SQL join can handle efficiently.

D: Similar to option C, using Cloud Dataflow to join data from a file in Cloud Storage is unnecessarily complex and costly. This approach is designed for large-scale ETL, not for efficient analytical querying.

1. Google Cloud Documentation - Introduction to external data sources: "Performance. Queries that include external tables might be slower than queries that only read BigQuery managed tables. If query speed is a priority, load the data into BigQuery instead of setting up an external data source." This directly supports why option A is superior to B for performance, which impacts cost.

2. Google Cloud Documentation - BigQuery Pricing - Loading and extraction: "Loading data... Batch loading data into BigQuery is free of charge." This confirms that the data ingestion method proposed in option A does not incur a cost, making it highly economical.

3. Google Cloud Documentation - Optimize query performance - Optimize your join patterns: The best practices for optimizing joins, such as ordering the largest table first, are most effective when all data resides within BigQuery's managed storage. This allows the query execution engine to have full control over data distribution and shuffling, which is not possible with external tables, reinforcing the choice of a native table for optimal join performance.