The author of the text describes a technique called "Mix and Batch" for fast, parallel relationship loading in Neo4j. The technique involves dividing start node IDs and end node IDs into non-overlapping sets, combining them to create partition codes, laying out these codes into a table, gathering the codes into batches with unique column and row IDs per batch, partitioning the data by the partition code within each batch, and loading one batch at a time. The author tested this approach with large datasets in Neo4j AuraDS environment using Apache Spark and achieved significant speed improvements, up to two to three times faster than traditional serial loading methods. This technique can be applied to streaming data, but requires further investigation and experimentation.