Flexible data delivery using a decentralized data mesh

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

A data mesh is a network of distributed data nodes linked together with smart APIs. This decentralized, modular architecture enables you to copy the data you want into data nodes. Then deploy and update individual nodes whenever necessary.

Data Nodes Smart APIs Data Portal

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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.

Data Map

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.

Algorithms

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

More on Data Science Integration

Smart API

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.

More on Smart APIs

Advantages of Data Nodes and Data Mesh

Agility

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.

Scalability

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.

Let’s get the conversation started

From a 30-minute demo to an inquiry about our 4-week pilot project, we are here to answer all of your questions!