Building a model for event data as a graph

Snowplow is exploring the potential of integrating graph-based storage targets for enriched event data, expanding beyond traditional relational databases. The initiative follows the recent launch of their Snowflake Loader and upcoming support for Google's BigQuery. The blog series aims to document their journey in modeling event data as graphs, considering different approaches like event-grammar and time-series, and examining how events can be represented as nodes or edges. Graph databases, such as Neo4j, offer advantages in simplifying complex queries, modeling networks, and enhancing the performance of applications like recommendation engines. These databases facilitate better alignment between business concepts and data structures, potentially aiding strategic decision-making. The series will also explore loading both atomic and modelled data into graph databases, starting with Neo4j, while keeping an open perspective on vendor-agnostic solutions.

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