The lightweight, modular data architecture enables you to continuously scale with existing and emerging data types.
Future proof your data with a data mesh architecture
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Modular Data Nodes
The data node is the fundamental building block of the modular data mesh architecture.
A data node is formed by connecting data, algorithms and a smart API. Each part of the node (data mapping, script integration, and analysis apps) can be worked on independently, promoting agile workflows. Additionally, each node is containerized to enable rapid deployment in any location.
These are steps to bring data into a node:
- Select data from any source, such as data warehouses, data lakes, and even flat files of emerging data types.
- The data needs to be ready for analysis (cleaned and processed).
- A basic entity is chosen and used as a basis for mapping.
- Automated parsing allows you to rapidly refine your map.
- Once data is fully mapped, it’s held in an in-memory data model so that it can be analysed efficiently by algorithms that are invoked by analysis apps.
The algorithms are computational methods invoked by analysis apps. The methods can be classical statistics, complex scripts, or your own models (R, Python, AI/ML).
Below are the statistics currently available:
- Hypergeometric test for categorical data and sets
- Student’s T and Mann-Whitney U tests for numeric distributions
- Univariate and Multivariate Linear Regression for numeric relationships
- Paired analysis for repeated (longitudinal) measures
- Matched analysis for control of confounding variables
- Cox regression for survival analysis
- K Means and DBSCAN clustering for segmentation
- PCA, t-SNE and UMAP for projection/embedding
- Fast event sequence queries
- Pathway (systems) analysis via Hypergeometric test and GSEA
- Gene signature analysis via ssGSEA
- Chi-square test for categorical data
- Logistic Regression and Random Forest (also many other options) for prediction/classification
- Pearson/Spearman correlation for numeric distributions
Reuse your own scripts or methods by simply integrating them into the node, allowing you to leverage the enterprise features of the portal. Below are supported integrations:
- R integration (run any R algorithm/test) within an analysis app
- Python integration (run any Python algorithm/test) within an analysis app
- Machine learninglibraries, such as the SMILE library
The smart API allows node-to-node communication and user-to-node interactions using a standard language.
- Examples of the node-to-node communication are:
- Using a node to annotate data sent from another node
- Using a node to monitor analysis apps usage by other nodes
- The user-to-node interaction is through the analysis apps which are embedded within the smart API.
Advantages of Data Nodes and Data Mesh
Each part of the node and each node in the mesh can be worked on independently. As each node is containerized, it can be deployed as soon as any changes are ready.
As new data arises, new nodes can be constructed and deployed to the mesh. The same node can be accessed by many portals and teams. This allows your organization to scale your data mesh as you grow.
Accelerate time to value
Get value from day one. As a single node with a single analysis app can be released within hours. This allows domain experts to instantly start asking and answering their own questions.