This code shows how a common multi-component GitLab can be deployed on Kubernetes cluster. Each component (NGINX, Ruby on Rails, Redis, PostgreSQL, and more) runs in a separate container or group of containers.
This code showcases the full power of Kubernetes clusters and shows how can we deploy the world's most popular website framework on top of world's most popular container orchestration platform.
In this code we provide a full roadmap the deployment of a multi-node scalable Cassandra cluster on Kubernetes. Cassandra understands that it is running within a cluster manager, and uses this cluster management infrastructure to help implement the application. Kubernetes concepts like Replication Controller, StatefulSets etc. are leveraged to deploy either non-persistent or persistent Cassandra clusters on Kubernetes cluster.
A Python application and tutorial that use Flask framework to provide a REST API to receive requests from the UI. The API then persists the data to a Cloudant database.
The Logistics Wizard is an end-to-end, smart supply chain management solution that showcases how to execute hybrid cloud, microservices, and predictive data analytics in the real world.
This code demonstrates deployment of a Microservices based application Game On! on to Kubernetes cluster. Game On! is a throwback text-based adventure built to help you explore microservice architectures and related concepts.
An application that monitors a Twitter feed and determines customer sentiment using IBM Watson Assistant, Tone Analyzer, Natural Language Understanding, as well as CloudantDB
This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to topic where they are consumed by a spark streaming job running on IBM BigInsights (hadoop).
Serverless bank check deposit processing with object storage and optical character recognition using Apache OpenWhisk powered by IBM Cloud Functions. See the Tech Talk replay for a demo.
This journey helps to build a complete end-to-end analytics solution using IBM Watson Studio. This repository contains instructions to create a custom web interface to trigger the execution of Python code in Jupyter Notebook and visualise the response from Jupyter Notebook on IBM Watson Studio.