What is the main characteristic of the Lambda architecture in data processing?

The main characteristic of the Lambda architecture in data processing is that it combines stream and batch processing functionalities. This architectural pattern was designed to handle massive volumes of data by allowing for both real-time data processing (stream processing) and more traditional data processing methods that handle batches of data (batch processing).

Lambda architecture effectively integrates these two approaches to take advantage of the strengths of each. The stream processing component enables the system to process data in real time and provide insights for immediate decision-making, while the batch processing component allows for comprehensive analysis of larger datasets over time. This combination allows for a more robust and flexible data processing system that can accommodate various use cases and data types.

The other options do not accurately describe the Lambda architecture. It does support real-time processing, but that is only a part of its functionality. It does not exclusively analyze static datasets, as it is designed to work with both real-time and historically collected data. Additionally, visual data representation is not a defining characteristic of the Lambda architecture; rather, the focus is on how data is processed and integrated.