Open source tools are first created and tested then packaged into containers for the use of the clients.
Containers are tested with externalized configuration with various options for performance
Containers are added to the Epiphany container repository only after they meet our testing standards
This reduces the plumbing associated with most Open source products presenting users with a sanitized , tested and preconfigured environment.

Epiphany encapsulates workflows in a secure ecosystem and access to these is given through a secure SSL channel.
Software components are validated and configured using security best practices.
Epiphany monitors these containers throughout their lifecycle to detect anomalies.

By using the concept of volume mounts Epiphany addresses the requirement of persistent systems.

The Epiphany architecture is built on a containerized microservices approach. Every component exists as an individual process, enabling discovery, auto-scaling and loose coupling.

We have DevOps playbooks which can deploy elasticsearch clusters along with Kibana and Logstash.
We have DevOps playbooks to install streaming data pipelines using Kafka, Apache Flume, MongoDB, Graphite and Hadoop.
Some of the other containerized components we provide for BI are Grafana, Graphite, Prometheus and Riemann.

Fraud detection using graph databases.
This solution uses containerized graph databases and streaming data components like Kafka, Neo4j, Graphx, etc.
Unstructured data is ingested and correlations are deduced, which helps in use cases like fraud detection, event correlation, customer journey etc.

This solution uses Google Tensor Flow components in loosely coupled containers to build recommendation engines that are based on supervised neural networks.
Finding patterns in unstructured data using unsupervised neural networks based clustering principles.