What method is used to handle large volumes of data in Alteryx workflows?

Utilizing data streaming and in-database processing optimizations is the method used to effectively handle large volumes of data in Alteryx workflows. This approach allows users to work with data that resides within databases without having to first extract it entirely, which can save significant time and system resources. Data streaming enables processing of data in real-time or near real-time, reducing memory overhead and improving speed since only the necessary data is brought into memory during the workflow execution.

In-database processing optimizations allow users to perform data manipulations where the data resides, which is especially beneficial for large datasets. This minimizes the data movement across systems, enhancing performance and scalability. Together, these techniques make it possible to efficiently process and analyze large datasets without compromising on performance.

The other methods mentioned may offer some level of utility but do not specifically address the unique challenges posed by large volumes of data as effectively as data streaming and in-database processing. For instance, batch processing and file exports can be helpful but might require the entire dataset to be handled at once, which can lead to inefficiency. Group-processing datasets into manageable sizes can facilitate handling large datasets, but it may not leverage the capabilities Alteryx offers for real-time data handling. Lastly, employing workflow performance evaluation tools