Big Data

Use knowledge graphs to discover open source package vulnerabilities

Use knowledge graphs to discover open source package vulnerabilities

Technology and infrastructure generate an enormous amount of data on a day-to-day basis. Building knowledge out of this data in various real-world domains can be a big challenge. This article describes how to derive concise and precise knowledge from data and use it to track vulnerabilities in the software stack. It presents challenges related to package security and vulnerability and how they can be addressed using a knowledge graph. After reading this article, you’ll understand the concept of the knowledge graph and how you can apply it to your domain.

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Benchmarking transparent versus 1GiB static huge page performance in Linux virtual machines

Benchmarking transparent versus 1GiB static huge page performance in Linux virtual machines

In this article, I examine the performance of two virtual machines (VMs) using huge pages in the Linux kernel. One VM is configured to use transparent huge pages (THP), which happens by default. The other is configured to use 1GiB static huge pages (SHP), which requires special configuration on the virtualization host and in the virtual machine definition.

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Integrate Red Hat Data Grid and Red Hat’s single sign-on technology on Red Hat OpenShift

Integrate Red Hat Data Grid and Red Hat’s single sign-on technology on Red Hat OpenShift

Using Red Hat Data Grid as an external cache for Red Hat’s single sign-on technology makes it possible for Data Grid to store data independent of the application layer. This way, Data Grid provides application elasticity, failover across data centers, and a reduced memory footprint.

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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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