Big Data

Knowledge meets machine learning for smarter decisions, Part 1

Knowledge meets machine learning for smarter decisions, Part 1

Drools is a popular open source project known for its powerful rules engine. Few users realize that it can also be a gateway to the amazing possibilities of artificial intelligence. This two-part article introduces you to using Red Hat Decision Manager and its Drools-based rules engine to combine machine learning predictions with deterministic reasoning. In Part 1, we’ll prepare our machine learning logic. In Part 2, you’ll learn how to use the machine learning model from a knowledge service.

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Build embedded cache clusters with Quarkus and Red Hat Data Grid

Build embedded cache clusters with Quarkus and Red Hat Data Grid

There are many ways to configure the cache in a microservices system. As a rule of thumb, you should use caching only in one place; for example, you should not use the cache in both the HTTP and application layers. Distributed caching both increases cloud-native application performance and minimizes the overhead of creating new microservices.

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Debezium serialization with Apache Avro and Apicurio Registry

Debezium serialization with Apache Avro and Apicurio Registry

In this article, you will learn how to use Debezium with Apache Avro and Apicurio Registry to efficiently monitor change events in a MySQL database. We will set up and run a demonstration using Apache Avro rather than the default JSON converter for Debezium serialization. We will use Apache Avro with the Apicurio service registry to externalize Debezium’s event data schema and reduce the payload of captured events.

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New features and storage options in Red Hat Integration Service Registry 1.1 GA

New features and storage options in Red Hat Integration Service Registry 1.1 GA

This article introduces new storage installation options and features in the Red Hat Integration service registry. The service registry component is based on Apicurio. You can use it to store and retrieve service artifacts such as OpenAPI specifications and AsyncAPI definitions, and for schemas such as Apache Avro, JSON, and Google Protobuf. We’ve provided Red Hat Integration’s Service Registry 1.1 component as a general availability (GA) release in Red Hat Integration 2020-Q4.

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Securely connect Quarkus and Red Hat Data Grid on Red Hat OpenShift

Securely connect Quarkus and Red Hat Data Grid on Red Hat OpenShift

The release of Red Hat Data Grid 8.1 offers new features for securing applications deployed on Red Hat OpenShift. Naturally, I wanted to check them out for Quarkus. Using the Quarkus Data Grid extension made that easy to do.

Data Grid is an in-memory, distributed, NoSQL datastore solution based on Infinispan. Since it manages your data, Data Grid should be as secure as possible. For this reason, it uses a default property realm that requires HTTPS and automatically enforces user authentication on remote endpoints. As an additional layer of security on OpenShift, Data Grid presents certificates signed by the OpenShift Service Signer. In practice, this means that Data Grid is as secure as possible out of the box, requiring encrypted connections and authentication from the first request. Data Grid generates a default set of credentials (which, of course, you can override), but unauthenticated access is denied.

In this article, I show you how to configure a Quarkus application with Data Grid and deploy it on OpenShift.

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AI software stack inspection with Thoth and TensorFlow

AI software stack inspection with Thoth and TensorFlow

Project Thoth develops open source tools that enhance the day-to-day life of developers and data scientists. Thoth uses machine-generated knowledge to boost the performance, security, and quality of your applications using artificial intelligence (AI) through reinforcement learning (RL). This machine-learning approach is implemented in Thoth adviser (if you want to know more, click here) and it is used by Thoth integrations to provide the software stack based on user inputs.

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Kubeflow 1.0 monitoring and enhanced JupyterHub builds in Open Data Hub 0.8

Kubeflow 1.0 monitoring and enhanced JupyterHub builds in Open Data Hub 0.8

The new Open Data Hub version 0.8 (ODH) release includes many new features, continuous integration (CI) additions, and documentation updates. For this release, we focused on enhancing JupyterHub image builds, enabling more mixing of Open Data Hub and Kubeflow components, and designing our comprehensive end-to-end continuous integration and continuous deployment and delivery (CI/CD) process. In this article, we introduce the highlights of this newest release.

Note: Open Data Hub is an open source project and a community Operator for building an AI-as-a-Service (AIaaS) platform on Red Hat OpenShift.

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