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CERN PGDay 2026 is here!

Submitted by madegior on

 After a successful first edition in 2025, CERN PGDay returns in 2026 as a regular gathering for PostgreSQL users and enthusiasts in Suisse Romande (western Switzerland). Co-organized by CERN and SwissPUG, the event offers a chance to connect, share ideas, and exchange experiences in the vibrant Geneva region — home to many international organizations across the public, private, and scientific sectors.

Kepler’s Mars Orbit Analysis with Python Notebooks & AI-Assisted Coding

Submitted by canali on
Johannes Kepler’s analysis of Mars’ orbit stands as one of the greatest achievements in scientific history, revealing the elliptical nature of planetary paths and establishing the foundational laws of planetary motion. In this post, you will explore how you can recreate Kepler’s revolutionary findings using Python’s robust data science ecosystem. 
 

Building an Apache Spark Performance Lab: Tools and Techniques for Spark Optimization

Submitted by canali on

Apache Spark is renowned for its speed and efficiency in handling large-scale data processing. However, optimizing Spark to achieve maximum performance requires a precise understanding of its inner workings. This blog post will guide you through establishing a Spark Performance Lab with essential tools and techniques aimed at enhancing Spark performance through detailed metrics analysis.

Enhancing Apache Spark Performance with Flame Graphs: A Practical Example Using Grafana Pyroscope

Submitted by canali on

TL;DR Explore a step-by-step example of troubleshooting Apache Spark job performance using flame graph visualization and profiling. Discover the seamless integration of Grafana Pyroscope with Spark for streamlined data collection and visualization.

 

Building a Semantic Search Engine and RAG Applications with Vector Databases and Large Language Models

Submitted by canali on

This blog post is about building a getting-started example for semantic search using vector databases and large language models (LLMs), an example of retrieval augmented generation (RAG) architecture. You can find the accompanying notebook at this link. See also the SWAN gallery.

Exploratory Notebooks for Deep Learning, AI, and Data Tools: A Beginner's Guide

Submitted by canali on

Are you looking at some resources to get you up to speed with popular Deep Learning and Data processing frameworks? This blog entry provides a curated collection of notebooks that will help you kickstart your journey.

You can find the notebooks at this link. See also the SWAN gallery.

Can High Energy Physics Analysis Profit from Apache Spark APIs?

Submitted by canali on

We are in a golden age for distributed data processing, with an abundance of tools and solutions emerging from industry and open source. High Energy Physics (HEP) experiments at the LHC stand to profit from all this progress, as they are data-intensive operations with several hundreds of Petabytes of data to collect and process.

Distributed application cache for Kubernetes running Java Hibernate applications with Oracle Coherence Community Edition

Submitted by vkozlovs on

While working on a data set it is important that it stays easily and quickly accessible. Hibernate second-level caching with Coherence offers applications a resource optimized solution that keeps frequently used data in memory, by distributing it among different application instances, or sharing it with one or more dedicated cache machines. This article describes the knowledge that we gained through using the Oracle Coherence Community Edition for Hibernate second-level caching and gives a general overview of how this product can be used with Java applications running on Kubernetes.

Author: Viktor Kozlovszky

Disclaimer

The views expressed in this blog are those of the authors and cannot be regarded as representing CERN’s official position.

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