AnyOLAP: Analytical Processing of Arbitrary Data-Intensive Applications without ETL
Abstract: The volume of data that is processed and produced by modern data- intensive applications is constantly increasing. Of course, along with the volume, the interest in analyzing and interpreting this data increases as well. As a consequence, more and more DBMSs and processing frameworks are specialized towards the efficient execution of long-running, read-only analytical queries. Unfortunately, to enable analysis, the data first has to be moved from the source application to the analytics tool via a lengthy ETL process, which increases the runtime and complexity of the analysis pipeline.
In this work, we advocate to simply skip ETL altogether. With AnyOLAP, we can perform online analysis of data directly within the source application and while it is running. In the proposed demonstration, the audience will get the chance to put AnyOLAP to the test on a set of data-intensive applications that are supposed to be analyzed while they are up and running. As the entire analysis pipeline of AnyOLAP will be exposed to the audience in form of live and interactive visualizations, users will be able to experience the benefits of true online analysis firsthand.
Code available: https://gitlab.rlp.net/fschuhkn/anyolap_public
Paper available: http://www.vldb.org/pvldb/vol14/p2823-schuhknecht.pdf