Some weeks back I introduced to a tutorial on how to analyse GitHub traffic. The tutorial combines serverless technology and Cloud Foundry to automatically retrieve statistics and store them in Db2. The data can then be accessed and analyzed using a Python Flask app. Today, I am going to show you how the web site is protected using OpenID Connect and IBM Cloud App ID.
Many government agencies and public administrations offer access to data, contributing to open data. Using IBM Watson Studio with Jupyter Notebooks and Apache Spark it is simple to retrieve, combine and analyze data from different sources. The result can be easily visualized. Learn what it takes with this IBM Cloud solution tutorial.
For my tutorial on automated data retrieval and analytics, I use IBM Cloud Functions to automatically fetch GitHub traffic statistics once a day. It is implemented as a serverless Python action. Because some Python packages are needed, the question was how to pack and create the action. In this blog post, I share my experiences.
In a new solution tutorial, I show you how to automatically retrieve and store GitHub traffic data the serverless way with IBM Cloud Functions and Db2. The data can then be analyzed via a Web app deployed to Cloud Foundry on IBM Cloud. The app is secured with App ID using OpenID Connect. The new service Dynamic Dashboard Embedded provides visualization of the views and clones of GitHub repositories.
Recently, I introduced you to a new tutorial for a database-driven Slackbot. Today, I am going to discuss security details, how the IBM Watson Conversation service is accessing a Db2 Warehouse service from within a dialog. It uses a serverless setup with IBM Cloud Functions. All the necessary credentials to execute the code and to access the Db2 database are automatically bound. Hence, the function code and the dialog don't need any account-specific changes and are generic.
A tutorial I wrote, featuring a database-backed Slack chatbot, is now live. It uses Db2 as database system to store event data. The client accessing the database is written in Node.js and is implement with IBM Cloud Functions in a serverless way. During the development of that tutorial I faced the question on how to perform the database setup. Should I guide users through the user interface to create a table and insert data? Should they install a Db2 client and execute a script locally? I solved the problem in a serverless fashion. Here are the details.
Ever wanted to build a Slackbot, a chatbot integrated into Slack, on your own? I am going to show you how easy it is to integrate Slack or Facebook Messenger with the IBM Watson Conversation service. As a bonus, the bot is going to access a Db2 database to store and retrieve data. The code in the tutorial uses a serverless fashion with IBM Cloud Functions.
If you follow my private blog you might remember that I have been using the IBM Watson Conversation service and DB2. My goal was to write a database-driven Slackbot, a Slack app that serves as chat interface to data stored in Db2. I will write more about that entire Slackbot soon, but today I wanted to share some chatbot tricks I learned. How to gather input data, perform checks and clean up the processing environment.