Which option is better for transmitting large text data (about 10 GB)?

When transmitting large text data of about 10 GB, it is important to consider efficiency, reliability, and scalability.

Best options:

1. Using streaming — instead of loading the entire file into memory, data is transmitted and processed in parts. This reduces memory load and allows processing to start earlier.

2. Storage and transmission via distributed file systems or cloud storage (e.g., HDFS, Amazon S3). Instead of direct network transfer, links can be shared or APIs used to access the data.

3. Using specialized protocols and formats — for example, gRPC with streaming, Apache Kafka for message transmission, or compression formats (gzip, snappy) to reduce data volume.

4. Chunking data — dividing into fixed-size parts that are transmitted and processed separately.

Example: if you are building an ML pipeline, it is better to store data in distributed storage and stream it, rather than trying to transfer the entire file over the network.

Thus, the optimal approach is streaming with distributed storage systems and protocols that support transmitting large volumes of data in parts.