What is the optimal method for ingesting very large amounts of data, such as terabytes?

Batch ingestion is the optimal method for ingesting very large amounts of data, such as terabytes, because it allows for the efficient processing of large datasets at once, rather than focusing on smaller increments of data. Batch ingestion is particularly well-suited for scenarios where data doesn’t need to be processed immediately and can be collected over a period before being ingested. This method supports high throughput, making it possible to handle extensive volumes of data in a way that optimizes resources and time.

In contrast, real-time ingestion is designed for scenarios that require immediate processing and responsiveness, often leading to increased complexity and resource demands, which may not be suitable for very large datasets. Manual data entry is impractical for handling large volumes due to its inherent limitations in speed and accuracy. Cloud-based ingestion, while useful in many scenarios, is a broader category that includes various ingestion methods, but it does not inherently address the nuances of efficiently managing large data volumes like batch ingestion does.

Therefore, for terabytes of data, the batch ingestion approach stands out as the most effective and practical solution.