In this article, we will look at the detailed steps to install Alibaba Cloud Container Service for Kubernetes (ACK) cluster. We will cover the Azure DevOps release pipeline and configure service connection to the Kubernetes cluster using temporary kubeconfig. After that, we will expose the application in Alibaba Cloud Kubernetes using Ingress.
High-level steps:
Modern data-driven applications are based on various data sources and complex data stacks that require well-designed frameworks to deliver operational efficiency and business insights. Data pipelines allow organizations to automate information extraction from distributed sources while consolidating data into high-performance storage for centralized access. In this Refcard, we delve into the fundamentals of a data pipeline and the problems it solves for modern enterprises, along with its benefits and challenges.
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If you and your team are dealing with tools like Git or Subversion, you may need an administrative layer where you are able to manage user access and repositories in a comfortable way, because source control management systems (SCM) don’t bring this functionality out of the box.
Perhaps you are already familiar with popular management solutions like GitHub, GitBlit or GitLab. The main reason for their success is their huge functionality. And of course, if you plan to create your own build and deploy pipeline with an automation server like Jenkins you will need to host your own repository manager too.
Oracle Transactional Business Intelligence (OTBI) is built on the power of Oracle’s industry-leading business intelligence tool Oracle Business Intelligence Enterprise Edition (OBIEE). This allows users to build powerful data visualization with real-time data that highlights data patterns and encourages data exploration instead of delivering static flat reports. OTBI provides users a wide variety of data visualization options from standard graphs to advanced visuals such as trellis, treemaps, performance tiles, KPIs, and others.
FlexDeploy has an innovative CI/CD solution for managing the build and deployment of OTBI WebCatalog objects across the pipeline. Using FlexDeploy’s partial deployment model, developers can assemble related catalog objects into packages, build them from source control or a development environment, and deploy them into the target environments.
Ever since Patrick Debois coined the word DevOps back in 2009, teams and organizations have been clamoring to adopt relevant practices, tools, and a sense of culture in a bid to increase velocity while maintaining stability. However, this race to incorporate “DevOps” in software development practices has resulted in a perversion of the concept. This does not mean that there are no successful practices of teams adopting DevOps practices, but the word overall has become a buzzword. As per the DORA 2019 State of DevOps report, team managers are more likely to proclaim that their teams are practicing DevOps compared to the actual frontline engineers and developers.
Therefore, this piece aims to realign the meaning of DevOps as well as highlight the need for considering debugging as a core element of the practices and cultures that enable DevOps for teams. The argument for debugging as a core component in the DevOps pipeline is a result of the evident need for a shift-left in the way we build and release software, empowering developers to adhere to the intrinsic principle of you build it you run it.
De nombreuses études (1) estiment que les organisations financières dédient plus de 50% de leur charge de travail à la collecte et l’analyse de données. C’est pourquoi l’automatisation est un outil à fort potentiel.
Avec la plateforme SAP S/4HANA, l’ERP nouvelle génération, SAP introduit un ensemble de techniques permettant à la machine d’imiter une forme d’intelligence réelle, l’Intelligence Artificielle.
Les domaines d’application sont nombreux et peuvent être mis au service de la transformation de la fonction finance.
La réconciliation bancaire est un exemple de processus qui implique de nombreuses actions manuelles, notamment pour les flux d’encaissements :
SAP introduit dans la suite SAP S/4HANA, une solution innovante basée sur du Machine Learning et permettant d’atteindre des taux d’automatisation proche de 97%.
La solution apprend des données et actions historiques, c’est-à-dire des répétitions (patterns) dans un ou plusieurs flux de données (extraits de comptes électroniques, avis de paiements, banques partenaires/sociétés,…) et en tire des prédictions de réconciliation en se basant sur des statistiques.
Les critères de réconciliation sont ainsi continuellement affinés et les nouveaux cas, les exceptions, traités en autonomie. Les taux d’automatisation considérablement améliorés contribuent à réduire les coûts de déploiements et coûts opérationnels.
L’évolution constante des modèles économiques impose aux directions financières de gagner en efficacité et en agilité. Elles doivent anticiper les impacts sur les revenus, la profitabilité, l’affectation des ressources afin d’orienter la stratégie de l’entreprise.
La plateforme SAP S/4HANA embarque des scénarios d’analyses prédictives permettant d’établir des prévisions de chiffre d’affaires très fiables via des algorithmes se basant sur des données actuelles et historiques, internes et externes à l’organisation tel que le pipeline des ventes, les tendances du marché, l’évolution du PIB d’un pays, etc.
Ces prédictions effectuées en temps réel alimentent le processus de planification budgétaire et sont déclinées à tous les niveaux de l’organisation où des ajustements peuvent être effectués.
A cela s’ajoutent des outils de simulation permettant de visualiser l’impact des choix stratégiques tel que les changements d’organisation, le développement de nouveaux produits, etc.
La crise sanitaire et économique inédite que nous vivons ouvre aux fraudeurs de nouvelles failles pour parvenir à leurs fins. De nombreuses attaques n’étant jamais identifiées, il est impossible de déterminer avec précision les pertes pour les entreprises. En 2020, d’après l’ACFE Report to the Nations (2), la fraude et la cybercriminalité ont causées des pertes estimées à plus de 3 Milliards € dans 125 pays (~5% du chiffre d’affaires des entreprises concernées, avec une moyenne de 415 K€ par attaque en Europe).
Avec SAP S/4HANA, SAP propose des solutions pour contrôler des volumes importants de transactions, identifier en temps réel les menaces et transactions frauduleuses et ainsi limiter les risques de pertes financières.
L’Intelligence Artificielle émet des recommandations permettant d’affiner continuellement les règles de contrôle et l’identification de nouveaux schémas de fraude sur la base de données historiques internes comme externes. Les taux d’efficacité dans la détection des anomalies et des fraudes se trouvent considérablement améliorés.
Au-delà des multiples scénarios disponibles en standard, SAP complète son offre SAP S/4HANA par une plateforme d’innovations dans le cloud (Business Technology Platform) qui permet de développer des applications et extensions autour du cœur SAP S/4HANA.
À travers la Business Technology Platform, les départements financiers ont accès aux technologies innovantes tel que le Machine Learning, l’analyse prédictive, les chatbots ou encore la Blockchain, à partir desquelles ils peuvent imaginer de nouveaux cas d’usages.
(1) A future that works: Automation, employment and productivity, Mckinsey&Company
(2) Report to the Nations, ACFE
The post L’Intelligence Artificielle pour accélérer vos processus financiers appeared first on SAP France News.
Creating a continuous deployment pipeline will bring us a step closer to an automated build, test, deploy strategy. In order to create such a pipeline, we need to have access to several tools. Instead of installing these on on-premise servers, we can make use of the AWS cloud offer. Let’s see how this can be accomplished!
We want to create an automated pipeline in order to ensure that no manual and error prone steps are required for building, testing and deploying the application. When a failure occurs during one of these steps, we will be automatically notified and can take necessary actions in order to resolve the issue.
When I use map apps such as Google Maps, I often use Street View to display more detailed views of a street or inside of a building. Although Google Street View is fantastic, there are many use cases where I want to control the development pipeline and assets without having to upload images to Street View.
To control and optimize your business workflow, you can use open-source Marzipano to build virtual tours yourself and host the tours on your own web site. This tutorial explains how to build the tour with open source software and host it for free on GitHub Pages. The technology and techniques can scale to large cloud businesses and are in production at commercial sites. This tutorial focuses on a minimal simplified site to get you started with the basic concepts in minutes.
In this article, we will see how to implement a data pipeline from an application to Mongo DB database and from there into an Elastic Search keeping the same document ID using Kafka connect in a Microservice Architecture. In recent days and years, all the microservices architectures are asynchronous in nature and are very loosely coupled. At the same time, the prime approach to have minimum code (minimum maintenance and cost), no batch systems (real-time data), and promising performance without data loss fear. Keeping all the features in mind Kafka and Kafka connect is the best solution so far to integrate different sources and sinks in one architecture to have very robust and reliable results.
We will Depp drive and implement such a solution using Debezium Kafka connect to achieve a very robust pipeline of data from one application into Mongo and then into Elastic cluster.
As more organizations move to establish DevOps techniques into their Software Development Life Cycle, the need of security becomes even more evident when so much application development is going on. But…
The idea of security in DevOps or DevSecOps doesn’t go very well with the classic DevOps process that insists on continuous integration, delivery, and deployment. When at production you’re constantly releasing smaller bits of your code and application using the DevOps pipeline, introducing security to DevOps can slow down the process significantly. You can’t just pass that through a security team that takes several weeks bringing the new release out to production.
