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.

In today’s post I will describe the process of integrating OIDC implicit flow with ORDS running on Tomcat against Keycloak service. May sound complicated, but we’ll break it down into individual components so we know what we’re talking about.

TLDR; Apache Spark 3.0 comes with many improvements, including new features for memory monitoring.

Apache ZooKeeper is an open-source server which enables highly reliable distributed coordination. Distributed applications can use it to maintain configuration information, implement naming, provide synchronization and group services.

In today’s post, we’ll be talking about the possible ways to manage the static images/CSS/JS that come shipped with APEX, when running on ORDS. They are separate resources (not contained in the DB like some other APEX images) necessary for your APEX applications look and behave the way they’re intended to. If you and your users browse your internet using lynx (see image below) feel free to skip this one. Otherwise - dig in! 

Creating PDFs in APEX after ORDS 19.1

Until 19.1 ORDS provided a built-in printing engine based on Apache FOP which allowed you to download a PDF version of your reports and XLS-FO templates in a very easy manner. However in ORDS 18.4.0 release notes we could find information that this feature is deprecated and will be removed in future release. This is exactly what happened with the release of ORDS 19.2.

So what actually happened? 

This is from Oracle’s release notes of ORDS 19.2:

Summary: This post details a solution for distributed deep learning training for a High Energy Physics use case, deployed using cloud resources and Kubernetes. You will find the results for training using CPU and GPU nodes. This post also describes an experimental tool that we developed, TF-Spawner, and how we used it to run distributed TensorFlow on a Kubernetes cluster.

Hi, my name is Priyanshu Khandelwal. I was amongst the 40 students selected from all over the world to work at CERN as an Openlab Summer Student 2019. I worked in the IT-DB-DAR section under the supervision of Mr Antonio Nappi.

Introduction

In the first part of the article we will provide an overview of how you can use Oracle REST Data Services for providing APIs directly from your PL/SQL code . The second part covers how to document our Web services using Swagger. Lets begin with a couple of technical concepts:

Introduction

Designing a multi-layer system is not rocket science, the difficulty can lie in selecting the right technologies. The main concept behind the design is to have better control and fine tuning of the components. This blog post will discuss the benefits & limitations of implementing this type of design and our practical experience gained from using it for the Open Days reservation system, which helped to welcome 75.000 people on our site and was hosted on the Oracle cloud using their cloud services.

Oracle REST Data Services running on Tomcat - Basic Authentication using JNDI Realm

What do we want to achieve?

Introduction

International organisations can have multiple official languages, in these cases usually their workflows/processes are designed to support that. CERN is one of those organisations, it's official languages are French and English. Therefore one of our tasks was to make the Open Days reservation system bilingual. In this article you will read about the choices we made to internationalize the system, what obstacles we faced and what solution we went for.

Introduction

Introduction

Authored By: Nazerke Seidan, Emil Kleszcz, Zbigniew Baranowski
Published By: CERN IT-DB-SAS

Authored By: Aman Pratap Singh and Prasanth Kothuri

Published By: CERN IT Hadoop and Spark Service

Topic: This post describes a data pipeline for a machine learning task of interest in high energy physics: building a particle classifi

These last days I've been playing with Apache Tomcat 9 and log4j2. You may wonder why I want to change the good an old Tomcat JULI. Well, although I love the simplicity of its configuration and the fact that JULI works, log4j2 offers a big variety of appenders that make it very interesting. Also I prefer the way of how log4j2 rolls the files, keeping the original name un-touched (e.g.

Topic: This post dives into the steps for deploying a performance dashboard for Apache Spark, using Spark