Data Layers

Just like Photoshop for your data

Users add Data Layers containing multi-dimensional graph data sets to their Graphmarts for data cleansing, transformation, semantic model alignment, relationship linking, and access control. Data Layers are used to dynamically enhance data in an iterative manner to drive continuous improvement of data richness, quality and connectivity.

Data Scientists and Citizen Data Scientists alike can use Data Layers to infer, create and connect data through business rules, Machine Learning and other popular predictive or statistical​analytics techniques as well as add remotely sourced data to the mix on-the-fly, so that the most up to the minute information necessary to complete an analysis through Federated Query ​Data Layer​ configuration.

Graphmarts apply Data Layers in a stacked fashion, affording the flexibility to discretely turn layers on or off, as well as refresh, remove, copy and create layers as needed. By storing data preparation tasks as individual layers, users enjoy dramatic improvements in data preparation productivity, similar to modern digital tools for editing images, video and sound. ​When made live in their Graphmart​​, individual ​Data Layers can be configured to be visible or invisible​ to the scope of an inbound graph query​ using that​​ ​query’s’ associated context​, thus supporting custom graph property level access controls and data masking schemes.​

Data Layers transform and manipulate data in-place within Anzo Graph Query Engine (AGQE) for a unique, non-linear iterative approach to data preparation. This late-binding ELT capability offers relief from brittle ETL processes and is a key differentiator in how Anzo creates the Semantic Layer.


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