For much of the last decade, enterprises fought against data silos, isolated persistence stores holding untold but inaccessible knowledge. Their primary weapon was the data lake: a huge centralized datastore that held terabytes of domain-specific data in a single logical location. By Jesse Menning.
Gartner’s conception of a data fabric relies heavily on the “backbone” of a knowledge graph. The knowledge graph describes the relationship between data sources throughout the entire fabric. Using this graph, machine learning and artificial intelligence determine the relationships between various sources of data and infer metadata automatically. The result is a catalog of data resources that can be used by consumers across the enterprise.
It turns out data lakes bring challenges of their own. The article then explains:
- Data mesh vs. data fabric
- What is a data fabric
- What is a data mesh
- Flavors of data products
- How to get started with an event-driven data mesh
Event-driven architecture at the transactional layer accelerates customer interaction, giving businesses a leg up on their competition. One layer down, an event-driven data mesh can do the same for analytics, decreasing the time it takes to get answers to crucial questions using data from across domains. The first steps down the path are to choose an approach (data fabric vs. data mesh) and pick an event-driven infrastructure that can support the initiative. Good read!
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