The question of what is the amount of data being processed needs to be answered on a case-by-case basis. The amount of data to be processed depends on the performance and storage capacity of the computer system used, as well as the complexity of the data processing task required. In general, smaller amounts of data can be processed in a shorter period of time, while larger amounts of data may take longer and require more computing power.

The following challenges and reasons may be encountered when processing large data volumes:

1. Computational resource constraints: Large data volumes may exceed the processing capacity of the computer system, resulting in slower processing or inability to complete the processing task.

2. Storage space constraints: Processing large data volumes requires sufficient storage space to store the data and intermediate results, otherwise it may lead to insufficient storage problems.

3. Algorithms and optimization issues: Processing large data volumes requires the use of efficient algorithms and optimization techniques to increase processing speed and reduce resource consumption.

4. Data transfer and network bandwidth: Larger data volumes may face data transfer and network bandwidth constraints if the data is distributed across different locations or needs to be transferred over a network.

In order to handle large data volumes, the following extensions can be considered:

1. Use of distributed computing: Split the data into smaller parts and assign them to multiple computers for parallel processing, thus increasing the processing speed.

2. Optimize algorithms and data structures: choose algorithms and data structures suitable for large data volume processing to reduce the consumption of computing and storage resources.

3. Increase computing and storage resources: use higher performance computer systems and increase storage capacity to meet the needs of large data volume processing.

4. Data compression and storage optimization: compression and optimization of data to reduce the occupation of storage space, and improve the efficiency of data reading and writing.

In summary, the size of the data volume to be processed depends on factors such as computational resources, storage space, and task complexity, and there are a number of challenges and limitations that may be faced in processing large data volumes. In order to handle large data volumes, there are a number of extensions that can be taken to improve processing efficiency and meet requirements.