Topic
Stream processing: Continuous data management in real time
Stream processing lets developers view, analyze, and combine data from a wide
Data Science
Article
Manage Python security with Thoth's cloud-based dependency resolver
Get a video introduction to Project Thoth's cloud-based Python dependency resolver, then learn how to manage Python dependencies on the Thoth command line.
Article
Data conversion in Pandas dataframes: 3 approaches to try
Data encoding is an important part of data preprocessing. Learn three tried-and-true approaches to data conversions in the Python Pandas library.
E-book
Open Source Data Pipelines for Intelligent Applications
Open Source Data Pipelines for Intelligent Applications provides data engineers and scientists insight into how Kubernetes provides a platform for building data platforms that increase an organization’s data agility.
Article
How to self-host a Python package index using Pulp
Learn how to set up a Pulp Python repository and publish and consume Python packages using Pulp on the Red Hat Developer Operate First cloud.
Article
Extracting dependencies from Python packages
Discover how to resolve Python dependencies by extracting metadata and dependency information, and how Project Thoth helps to streamline the process.
Article
More machine learning with OpenShift Data Science
Solve the typical data science problems of accessing Amazon S3 data and creating a TensorFlow model by following two new OpenShift Data Science learning paths.
Article
Prevent Python dependency confusion attacks with Thoth
Discover how Project Thoth solves dependency management issues and vulnerabilities in the Python ecosystem, making the resolution process cloud-based.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Topic
Open Data Hub: Build a platform for AI-as-a-service (AIaaS)
Collecting and understanding the data gathered through AI/ML workloads.
Topic
Data Science: Deploy machine learning (ML) workloads
Managed cloud services provide stable training and workload platforms for
Article
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...
Article
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...
Article
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...
