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Red Hat Developer roundup: Best of November 2021
Red Hat Developer shares its readers' top picks of articles posted in November 2021, including two Linux updates. See the full recap at the end of this article.
Data science
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Build and extend containerized applications with Project Thoth
Learn how to better build and extend containerized apps by using Project Thoth to control container image quality and provide more robust runtime environments.
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Build and deploy an object detection model using OpenShift Data Science
Watch this demo by Red Hat Principal Software Engineer Chris Chase on how to build and deploy an object detection model within an intelligent application.
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Access more data from your Jupyter notebook
Pull data without duplicating it into a Jupyter notebook using Starburst Galaxy, a fully managed data platform built on Red Hat OpenShift Data Science.
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Building machine learning models in the cloud
Get hands-on resources for building machine learning models using Red Hat OpenShift Data Science. Learn how to use NLP, Jupyter notebooks, and more.
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Thoth prescriptions for resolving Python dependencies
Thoth prescriptions resolve dependency conflicts in Python application stacks. Learn how to create and use them to build healthier Python applications.
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Open Data Hub: Build a platform for AI-as-a-service (AIaaS)
Collecting and understanding the data gathered through AI/ML workloads.
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Data Science: Deploy machine learning (ML) workloads
Managed cloud services provide stable training and workload platforms for
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Scaling Sync
Introduction One of the biggest challenges for developers to build mobile applications is data synchronization. It's the foundation for many different types of mobile applications, but it's very complicated and very hard to implement. This can be even harder for enterprise developers, as often they have to make sure the data is not only synchronized to the server side of their mobile apps but also synchronized to the database backends of their enterprises, as demonstrated in this diagram: That's why...
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External materialized views demystified in Red Hat JBoss Data Virtualization and Red Hat JBoss Data Grid
Red Hat JBoss Data Virtualization (JDV) provides several capabilities for caching data including: materialized views, result set caching, and code table caching. These techniques can be used to significantly improve performance in many situations. With the exception of external materialized views, the cached data is accessed through the BufferManager. For better performance, the BufferManager setting should be adjusted to the memory constraints of your installation. See the Admin Guide for more on parameter tuning. JDV supports two kinds of caching...
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What's new in Red Hat JBoss Data Virtualization 6.3
We are happy to announce the availability of Red Hat JBoss Data Virtualization (JDV) 6.3 GA. JDV 6.3 release focuses on three areas: In-memory technologies for Big Data processing Development and deployment productivity Big Data and Cloud data sources The following new features and data source integration were added in support of these themes: In-memory technologies for Big Data processing Apache Spark - Apache Spark is an open source big data processing framework built around speed, ease of use, and...
