1. Dataset Generation

• Use a script to create the dataset with English, Spanish, and French entries.
• For each entry, add a timestamp indicating when it would be processed in the simulated real-time environment.
• As required, populate fields like text, timestamp, language, user_info, and metadata.

2. Timestamp Fixing

• If we need realistic timestamp sequences, we can generate them at regular intervals (e.g., every few seconds) to mimic live data flow. For a diverse spread:
  • Generate timestamps chronologically by incrementing each new entry by a set interval.
  • If the data is pre-existing without timestamps, use a script to add them in sequence.
  • Python’s datetime library can help create and manage these timestamps.

3. Testing with Real-Time Simulation

• Set up the Kafka or Kinesis stream.
• Write a data loader script that reads entries from the custom dataset at the specified intervals and sends them as messages to Kafka or Kinesis.
• For a delayed stream, use a time delay (time.sleep) in the script based on the timestamp difference between entries.