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Based on our record, Open Data Hub should be more popular than OrientDB. It has been mentiond 3 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
First, you need to choose a specific graph database platform to work with, such as Neo4j, OrientDB, JanusGraph, Arangodb or Amazon Neptune. Once you have selected a platform, you can then start working with graph data using the platform's query language. - Source: dev.to / about 2 years ago
Perhaps have a look at OpenDataHub. While geared for Openshift, see if they solved some of your concerns. Source: almost 2 years ago
A common approach is to deploy JupyterHub on Kubernetes and configure it for Elyra, like it is done in Open Data Hub on the Red Hat OpenShift Container platform. - Source: dev.to / over 4 years ago
If you are interested in running pipelines on Apache Airflow on the Red Hat OpenShift Container Platform, take a look at Open Data Hub. Open Data Hub is an open source project (just like Elyra) that should include everything you need to start running machine learning workloads in a Kubernetes environment. - Source: dev.to / over 4 years ago
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
Presto DB - Distributed SQL Query Engine for Big Data (by Facebook)
neo4j - Meet Neo4j: The graph database platform powering today's mission-critical enterprise applications, including artificial intelligence, fraud detection and recommendations.
IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
C3 AI Suite - The C3 AI Suite uses a model-driven architecture to accelerate delivery and reduce the complexities of developing enterprise-scale AI applications.