Apache Fluo is an open source implementation of Percolator  (which populates Google’s search index) for Apache Accumulo . With Fluo, users can continuously join new data into large existing data sets without reprocessing all data. Unlike batch and streaming frameworks, Fluo offers much lower latency and can operate on extremely large data sets .
When combining new data with existing data, Fluo offers reduced latency when compared to batch processing frameworks (e.g Spark, MapReduce).
Incremental updates are implemented using transactions which allow thousands of updates to happen concurrently without corrupting data.
Avoid Reprocessing Data
Combine new data with existing data without having to reprocess the entire dataset.
Fluo applications consist of a series of observers that execute user code when observed data is updated.
The core Fluo API supports simple, cross-node transactional updates using get/set methods.
The Fluo Recipes API builds on the core API to offer complex transactional updates.
Apache Fluo graduated from the Apache Incubator to become a Top-Level Project at Jul 2017. The learning curve of such technologies for newcomers is not easy. However, the community has created a tutorial for and a skeleton project for it. One can follow Fluo Tour  to learn how you could use Fluo. You can fork the source code from Apache Fluo GitHub repository . Also, it has an active community and new contributors are usually mentioned on Twitter by @ApacheFluo.