Mapping ETL Dependencies with Neo4j

Contributed Talk | Day 2 | 2:20 pm | 40 Minute Duration | Grand Gallery C

Mapping ETL Dependencies with Neo4j

Contributed Talk | Day 2 | 2:20 pm | 40 Minute Duration | Grand Gallery C

Graph databases provide a powerful data modeling technique that every developer and data architect should have in their toolbox.  They offer increased performance when dealing with connected data, provide a flexible approach that allows structure and schema to emerge in step with the growing understanding of a problem space, and their mode of delivery aligns well with agile software delivery practices.

This presentation provides an example of how a graph database can be used to improve the development and implementation of a data warehouse that is in turn being used to manage an automated warehouse.  The presentation will begin with a brief introduction on labeled graphs followed by a description of the warehouse automation domain including the data warehouse extract, transform and load (ETL) processes.  It will then cover several use cases on how a graph database can be used to conduct an impact analysis as changes are made to the ETL process, troubleshoot deployment and configuration issues, and identify source data needed for dashboards. Individuals attending this presentation will be introduced to labeled graphs, data warehousing ETL, graph modeling, use cases for data dependency mapping, how to setup a graph database, and how to display and analyze graph data.