Some examples of open source ETL tools for data transmission are Apache Storm, Spark Streaming, and WSO2 Stream Processor. While these frameworks work in different ways, everyone is able to listen to message flows, process the data, and save it for storage. The first real-time analysis tool is Google Cloud DataFlow. Google recently excluded Python 2 and pushed Cloud DataFlow with the Python SDK and Python 3 to support data transmission.
Using streaming analytics in Google Cloud DataFlow helps filter out ineffective data that can slow down the speed of analysis. In addition, users can also use Apache Beam with Python to define data pipelines to ensure the extraction, transformation and analysis of data from different IoT devices and additional data sources. They need real-time data to support services such as fraud detection, trading platforms, ridesharing apps, and e-commerce websites. Many organizations are trying to collect as much data as possible about their products, services, or even their organizational activities, for example, by tracking employee activities using various methods, such as tracking records and taking screenshots at regular intervals.
As a result, companies could reap the maximum benefits of batch and streaming data analysis.
