  Modern ETL streaming pipelines extract data from various sources and forward
it to multiple consumers, such as data warehouses (DW) and analytical systems
that leverage machine learning (ML). However, the increasing number of systems
that are connected to such pipelines requires new solutions for data
integration. The canonical (or common) data model (CDM) offers such an
integration. It is particular useful for integrating microservice systems into
ETL pipelines. (Villaca et al 2020, Oliveira et al 2019) However, a mapping to
a CDM is complex. (Lemcke et al 2012) There are three complexity problems,
namely the size of the required mapping matrix, the automation of updates of
the matrix in response to changes in the extraction sources and the time
efficiency of the mapping. In this paper, we present a new solution for these
problems. More precisely, we present a new dynamic mapping matrix (DMM), which
is based on permutation matrices that are obtained by block-partitioning the
full mapping matrix. We show that the DMM can be used for automated updates in
response to schema changes, for parallel computation in near real-time and for
highly efficient compacting. For the solution, we draw on research into matrix
partitioning (Quinn 2004) and dynamic networks (Haase et al 2021). The DMM has
been implemented into an app called Message ETL (METL). METL is the key part of
a new ETL streaming pipeline at EOS that conducts the transformation to a CDM.
The ETL pipeline is based on Kafka-streams. It extracts data from more than 80
microservices with log-based Change Data Capture (CDC) with Debezium and loads
the data to a DW and an ML platform. EOS is part of the Otto-Group, the
second-largest e-commerce provider in Europe.
