Articles

The Open Source Way to Rightsize Kubernetes With One Click

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Rightsizing resource requests is an increasing challenge for teams using Kubernetes—and especially critical as they scale their environments. Overprovisioning CPU and memory lead to costly overspending, but underprovisioning risks CPU throttling and out-of-memory errors if requested resources aren’t sufficient. Dev and engineering teams that don’t thoroughly understand the live performance profile of their containers will usually play it safe and request vastly more CPU and memory resources than required, often with significant budget waste.

The open source Kubecost tool (https://github.com/kubecost) has had a Request Sizing dashboard to help Kubernetes users bring more cost efficiency to their resource requests. One of the tool’s most popular optimization features, the dashboard identifies over-requested resources, offers recommendations for appropriate per-container resource requests, and estimates the cost-savings impact of implementing those recommendations. The dashboard utilizes actual usage data from live containers to provide accurate recommendations. However, leveraging the dashboard has included some hurdles, requiring users to manually update YAML requests to align resource requests with Kubecost recommendations or introduce integrations using a CD tool. 

Source de l’article sur DZONE

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Why Do You Need Managed AI?

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Today no less than 60% of companies are either exploring the possibilities of adopting artificial intelligence or trying to realize its potential to transform the way they do business. The problem is that a significant portion of them (one-third) struggle to produce substantial change with AI.

The lifecycle of an AI solution usually consists of problem definition, data collection, model building, model fine-tuning, and applying the solution to solve a specific problem. Various experts build the solution to solve business problems. Still, a problem solved by a data scientist does not automatically translate into a constant stream of actual value for the business. Once deployed to production, the AI solution cannot be left as-is. Like any other system, it requires continuous maintenance. However, any AI solution’s maintenance differs significantly from the maintenance of other systems (e.g., microservice-based applications). The performance of any AI solution can be affected by many factors, and if the maintenance work is not done, the solution will cause problems instead of solving them.

Source de l’article sur DZONE

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Nomination d’Olivier Nollent au poste de Managing Director de SAP France

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Levallois-Perret, le 21 juillet 2022. SAP annonce la nomination d’Olivier Nollent au poste de Managing Director de SAP France. Olivier Nollent reporte à Rohit Nagarajan, Président EMEA North de SAP.

Olivier Nollent dirigera l’activité de l’une des plus importantes filiales commerciales de SAP, leader mondial des logiciels d’entreprise. Il aura pour mission :

  • de renforcer les synergies avec les clients, ainsi qu’avec l’écosystème de partenaires, dans leurs grands enjeux de transformation numérique (gestion de l’expérience, gestion intelligente des dépenses, réduction de l’empreinte sur l’environnement…), et d’innovation (intelligence artificielle, big data, blockchain…) grâce à ses technologies avancées et ses centres de R&D (SAP Labs) et avec son accélérateur de startups SAP.iO basé en France.
  • de poursuivre la croissance de SAP en France via le développement de ses activités Cloud auprès des grands comptes et des PME. Il s’appuiera à cet effet sur l’expertise du Groupe dans les domaines des logiciels ERP, Analytics, Supply Chain Management, RH et de gestion de l’expérience client, ainsi que sur le plus large portefeuille de solutions modulaires disponibles sur site, Cloud ou hybrides.

Olivier est diplômé de l’INSEEC et a débuté sa carrière chez HP, puis a passé 13 années chez Microsoft où il a occupé avec succès plusieurs rôles de direction commerciale, jouant un rôle clé dans la transition de l’entreprise vers le Cloud.

En avril dernier, après 5 années chez Salesforce, il rejoint SAP en tant que Senior Vice President Industries.

Son expérience des grands acteurs du secteur, sa solide expertise dans le développement des business de la Tech et son leadership éprouvé auprès de grandes organisations commerciales sont autant d’atouts pour accompagner la dynamique de croissance de SAP France.

Gérald Karsenti reste Président du Conseil d’Administration, assurant la direction générale de l’entreprise. Il accompagnera également Olivier Nollent dans sa transition vers son nouveau poste, en consolidant la notoriété de SAP sur le marché et en cultivant les relations avec ses clients et son écosystème.

The post Nomination d’Olivier Nollent au poste de Managing Director de SAP France appeared first on SAP France News.

Source de l’article sur sap.com

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What Is Text Classification?

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What Is Text Classification?

Text Classification is the process of categorizing text into one or more different classes to organize, structure, and filter into any parameter. For example, text classification is used in legal documents, medical studies, and files, or as simple as product reviews. Data is more important than ever; companies are spending fortunes trying to extract as many insights as possible.

With text/document data being much more abundant than other data types, new methods of utilizing them are imperative. Since data is inherently unstructured and extremely plentiful, organizing data to understand it in digestible ways can drastically improve its value. Using Text Classification with Machine Learning can automatically structure relevant text in a faster and more cost-effective way.

Source de l’article sur DZONE

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Machine Learning and Data Science With Kafka in Healthcare

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IT modernization and innovative new technologies change the healthcare industry significantly. This blog series explores how data streaming with Apache Kafka enables real-time data processing and business process automation. Real-world examples show how traditional enterprises and startups increase efficiency, reduce cost, and improve the human experience across the healthcare value chain, including pharma, insurance, providers, retail, and manufacturing. This is part five: Machine Learning and Data Science. Examples include Recursion and Humana.

Blog Series – Kafka in Healthcare

Many healthcare companies leverage Kafka today. Use cases exist in every domain across the healthcare value chain. Most companies deploy data streaming in different business domains. Use cases often overlap. I tried to categorize a few real-world deployments into different technical scenarios and added a few real-world examples:

Source de l’article sur DZONE

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Building a REST Service That Collects HTML Form Data Using Netbeans, Jersey, Apache Tomcat, and Java

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The Jersey project is very well documented so it makes it easy to learn REST with Java. In this article I’m going to build two projects. The first project will be a very simple HTML page that presents a form to the user and then submits it to a REST project residing on the same server. The second project will be the REST part.

For this article I used the following tools:
1. Netbeans 7
2. Apache Tomcat 7
3. Jersey
4. Java

Source de l’article sur DZONE

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Securing Kubernetes Secrets With HashiCorp Vault

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As more and more organizations making the shift to cloud-native technologies, Kubernetes has become the de facto choice to orchestrate container-based applications. As applications grow in size, the number of microservices increases and so does the data they process. Hence, handling data, especially sensitive data becomes critical. Out of the box, Kubernetes supports « Secrets » objects to store sensitive information — like passwords, tokens, ssh keys, and so on — securely.

Kubernetes secret eliminates the need to hard-code sensitive data in the application code. Secrets provide this sensitive information as data mount or expose them as environment variables.

Source de l’article sur DZONE

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Bring Streaming to Apache Cassandra with Apache Pulsar

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Twitch, YouTube, Instagram, Facebook — virtually every major brand nowadays uses live streaming to connect and engage their audience. For enterprises and developers building cloud-native applications, this growing trend creates a need for streaming technologies that can reliably handle the rush of massive amounts of data, while also being flexible and easy to manage for developers.

One such technology is Apache Pulsar® — an open-source, distributed messaging and streaming platform that’s easy to deploy, simple to scale, and packed with developer-friendly APIs. So the next question is: how can you stream from Pulsar to Apache Cassandra®, the powerful NoSQL database designed to support data-heavy applications in the cloud?

Join our beginner-friendly Pulsar workshop on YouTube and learn how to connect Pulsar with Cassandra for streaming! In this post, we’ll set the scene with an introduction to Pulsar and guide you through four hands-on exercises where you’ll use these free, cloud-native technologies: Katacoda, Kesque, GitPod, and DataStax Astra DB. Each exercise will also be linked to the step-by-step instructions on the DataStax Developers GitHub wiki.

Source de l’article sur DZONE

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Kafka vs. JMS: Which One Should You Be Using?

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Two of the most popular message brokers used today are Kafka and those based around JMS. JMS is a long-standing Java API used generally for developing messaging applications, with its primary function of being able to send messages between two or more clients. Kafka, on the other hand, is a distributed streaming platform that provides a lot of scalabilities and is useful for real-time data processing. 

While both offer their own advantages and are highly useful in their own right, which of the two should you be actually using?

Source de l’article sur DZONE

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A Complete Guide to Generated Columns in MySQL

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MySQL generated columns pose as a powerful, easy-to-use, and advanced tool for anyone who wants to add automatically generated data to their tables – in this blog, we will learn everything you need to know to master them.

Generated columns allow you to store automatically generated data in a table without using the INSERT and UPDATE clauses. This useful feature has been part of MySQL since version 5.7, and it represents an alternative approach to triggers when it comes to generating data. Also, generated columns can help you make your query easier and more efficient.

Source de l’article sur DZONE

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