Tellius architecture

Overview of the Tellius architecture, including the Web UI, search engine, ML engines, distributed storage, and scalable deployment across on-prem or cloud environments.

The Tellius architecture is designed to be user-friendly and easily accessible, allowing for seamless integration with existing systems.

Tellius Architecture

The Tellius architecture consists of the following components:

• The main entry point to Tellius is the HTML5-based Web UI, which runs in a web browser.

• Natural Language Search allows users to input natural language queries, which the engine then converts into backend queries for processing.

• The Genius Insights engine uses advanced machine learning algorithms to automatically discover actionable insights.

• The Predictive Machine Learning engine enables the training of custom machine learning models for making predictions

• Distributed Filesystem is responsible for storing intermediate data generated by Machine Learning and Genius Insights engine. Optionally, it can be used for storing data when explicitly enabled.

• Fast Query Database is a columnar store for storing data in indexed format for real-time search and visualization on big data. This is a cost-effective option when data cannot fit in available memory and high performance is still desired.

Deployment architecture of Tellius

Tellius can be deployed on-premises behind a firewall or in any of the major cloud technologies such as,

• Amazon Web Services

• Microsoft Azure

• Google Cloud

Here is an illustration of the deployment architecture of Tellius:

Deployment architecture of Tellius

The components are,

1. Kubernetes Cluster: It is a cluster of Kubernetes on one or more nodes.

2. Tellius Microservices: All the services related to Tellius will be running on the Kubernetes cluster, including,

   • Fast Query Engine (FQE) microservice, which is responsible for running the FQE engine cluster to handle high-performance data querying.

   • In-Memory Compute Engine (ICE) microservice, which is responsible for running an ICE engine cluster to handle computationally intensive machine learning tasks

Other auxiliary services, like Tellius Web UI and Python environment, are also available.