Real-time Data Ingestion

Real-time data ingestion is the process of ingesting data as it is generated, allowing for immediate analysis and processing. Unlike batch data ingestion, which processes data in scheduled intervals, real-time data ingestion enables organizations to react quickly to changing data streams and make timely decisions.

In order to perform real-time data ingestion, you need to:

• Read data from a real-time source: This could be a streaming platform, message queue, or any other source that continuously produces data.

• Process the data: Once the data is received, it needs to be processed in real-time to extract valuable insights. This could involve cleaning the data, applying transformations, or performing complex calculations.

• Load the data into a target system: The processed data needs to be loaded into a target system, such as a database or a data warehouse, where it can be further analyzed or used for real-time applications.

Here's an example of how to perform real-time data ingestion using Python:

1{{code}}

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# Python logic here

import time

​

# Simulating real-time data ingestion

while True:

    # Read data from a real-time source

    data = read_data()

    # Process the data

    processed_data = process_data(data)

    # Load the data into a target system

    load_data(processed_data)

    # Delay for a specified interval

    time.sleep(5)