Carol McDonald
  • Female
  • Jacksonville, Florida
  • United States
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What are your main interests in software development?
Java, Web, Ajax, Java EE, Spring, Seam, JPA, Web Services
Do you have a website?
http://weblogs.java.net/blog/caroljmcdonald/
Anything else you'd like to add? Where do you live? (optional!)
Je parle Francais. Ich kann Deutsch.

Carol McDonald's Blog

movie recommendations with Spark machine learning

Posted on August 4, 2015 at 11:15am 0 Comments

Tutorial on Apache Spark, movie recommendations with machine learning 

This post discusses building a recommendation model from movie ratings using an iterative algorithm and parallel processing with Apache Spark MLlib.

https://dzone.com/links/parallel-and-iterative-processing-for-machine-lear.html

An Inside Look at the Components of a Recommendation Engine

Posted on April 13, 2015 at 9:14am 1 Comment

Recommendation engines help narrow your choices to those that best meet your particular needs.  In this post, we’re going to take a closer look at how all the different components of a recommendation engine work together. We’re going to use collaborative filtering on movie ratings data to recommend movies. The key components are a collaborative filtering algorithm in Apache Mahout to build and train a machine learning model,…

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screencast about MySQL for Developers

Posted on March 30, 2009 at 10:30am 0 Comments

Here is a screencast about MySQL for Developers



If you are a developer using MySQL, you should learn enough to take advantage of its strengths, because having an understanding of the database can help you develop better-performing applications. This session will talk about MySQL database design and SQL tuning for developers. Some topics include:



* MySQL Storage Engine Architecture

* Schema, the basic foundation of performance

* Think about performance when… Continue

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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.

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InfoQ Reading List

Netflix Automates RDS PostgreSQL to Aurora PostgreSQL Migration Across 400 Production Clusters

Netflix engineers describe an internal automation platform that migrates nearly 400 RDS PostgreSQL clusters to Aurora, reducing downtime and operational risk. The platform coordinates replication, CDC handling, controlled cutover, and rollback, while supporting service teams in a self-service migration workflow.

By Leela Kumili

Presentation: 4 Patterns of AI Native Development

Patrick Debois discusses the evolution of software engineering in the age of AI. He shares four key patterns: transitioning from producer to manager, focusing on intent over implementation through spec-driven development, moving from delivery to discovery, and managing agentic knowledge. He explains how these shifts redefine seniority, team roles, and the future of the DevOps workflow.

By Patrick Debois

Java News Roundup: Apache Solr 10, LangChain4j, Grails, JobRunr, Gradle, Devnexus, Commonhaus

This week's Java roundup for March 2nd, 2026, features news highlighting: the GA release of Apache Solr 10; point releases of LangChain4j, JobRunr, Multik and Gradle; maintenance releases of Grails and Keycloak; Devnexus 2026; and Pi4J joining the Commonhaus Foundation.

By Michael Redlich

Podcast: Mindful Leadership in the Age of AI

In this episode, Thomas Betts and Sam McAfee discuss how AI hype is reshaping organizational behavior, why many companies struggle with experimentation, and how unclear decision structures create friction. They explore psychological safety and mindful leadership as essential foundations for healthier, more effective engineering cultures.

By Sam McAfee

Article: Change as Metrics: Measuring System Reliability Through Change Delivery Signals

System changes are the primary driver of production incidents, making change-related metrics essential reliability signals. A minimal metric set of Change Lead Time, Change Success Rate, and Incident Leakage Rate assesses delivery efficiency and reliability, supported by actionable technical metrics and an event-centric data warehouse for unified change observability.

By Peihao Yuan

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