BI-Architektur – Konzepte und praktische Umsetzung – Winfwiki Aus Winfwiki 1 Einleitung 1.1 Zielsetzung und Abgrenzung Ziel der Fallstudie ist es in einem ersten Schwerpunkt, die Konzepte, die sich hinter dem Begriff "Business Intelligence" verbergen, zu beleuchten und ein Grundverständnis zunächst einmal des Begriffes selbst und anschließend der Architektur moderner BI-Systeme im Allgemeinen zu schaffen. Dabei liegt der Fokus auf den allgemeingültigen theoretischen Architekturkonzepten der Business Intelligence und es werden keine konkreten herstellerspezifischen Konzepte betrachtet. In einem zweiten Schwerpunkt werden Konzepte der praktischen Umsetzung vorgestellt, d.h. unter welchen Voraussetzungen sind gewisse Architekturkonzepte gut geeignet oder weniger gut geeignet. 1.2 Vorgehensweise Die Fallstudie hat zwei Themenschwerpunkte: "Konzepte" und "praktische Umsetzung". 2 Business Intelligence 2.1 Begriffsklärung Business Intelligence (Abkz. 2.2 Geschichte 2.3 Anwendungsgebiete für BI Abb. 1: BI-Framework 3 Architekturkonzepte 3.1 Fachliche Architektur
Débuter avec Docker et les containers sous Debian 8 | Application (Linux) I. Présentation de Docker Aujourd’hui, nous allons parler container avec la solution Docker. L’objectif de Docker n’est pas la création de machines virtuelles, il s’agit là de la création de container, mais alors quelle est la différence ? Nous verrons cela en début d’article, pour que tout cela soit clair dès le départ. En fait, Docker a pour objectif de faciliter le déploiement d’applications, d’avoir plusieurs versions d’une même application sur un son serveur (phase de développement, tests), mais aussi d’automatiser le packaging d’applications. De plus, Docker permet de garder son système de base propre, tout en installant de nouvelles fonctionnalités au sein de containers. Dans ce tutoriel, nous verrons ce qu’est Docker, la différence entre une VM et un container, mais également comment installer Docker et comment créer son premier container avec Docker. Note : Docker nécessite une installation 64 bits de votre distribution pour fonctionner. II. Source : Docker.io – CONTAINER vs VM
Microservice Architecture - A Quick Guide 1. What is Architecture (Software)? Architecture is the fundamental organization of a system embodied in its components (i.e. 2. Microservice means developing a single, small, meaningful functional feature as single service, each service has it’s own process and communicate with lightweight mechanism, deployed in single or multiple servers. 3. 4. Microservice architecture brings a lot of operations overhead.Duplication of Effort.Distributed System is complicated to manage .Default to trace problem because of distributed deployment.Complicated to manage whole products when number of services increases. 5. When you need to support Desktop, web , mobile, Smart TVs, Wearable, etc... or you don't know in future which kind of devices you need to support. 6. Most large scale web sites including Twitter, Netflix, Amazon and eBay have evolved from a monolithic architecture to a microservices architecture. 7. 8. 9. 10. 11. Reference:
Did you Buy a Self-Service BI Fantasy? As I gaze outside my quiet home office window watching an alligator in the pond, I am reminded of my mostly isolated virtual reality. Not even a rare splurge on chocolate chip cookie dough cheered me up. Thank goodness for IT/Dev Connections next week! Even introverts need to socialize a little bit to stay sane. Speaking of sanity, I continue to hear insane expectations that self-service BI tools can replace the need for a data warehouse. “I like nonsense, it wakes up brain cells. Accurate Reporting Data Models have Not Changed While contemporary self-service BI tools are totally amazing and revolutionizing business intelligence everywhere with rapid, simple, visual reporting for everyone, data model design patterns for reporting accurate values over time, across multiple data sources in an organization have not changed. Self-service BI tools in the market today are ideal for personal and team level reporting, quick prototypes and fire-drill, on the spot insights.
Programmation Java/Énumérations kkkk Un livre de Wikilivres. Java 5 introduit une nouvelle structure de données appelée énumérations. Cette structure permet de contenir une série de données constantes ayant un type sûr, ce qui veut dire que ni le type, ni la valeur réelle de chaque constante n'est précisé. Exemple : Membres[modifier | modifier le wikicode] Les énumérations sont en fait compilées sous forme de classes, éventuellement internes. Une énumération peut donc avoir des constructeurs et méthodes. Méthodes utiles[modifier | modifier le wikicode] Les énumérations possèdent des méthodes communes. ordinal() Obtenir l'index de la valeur selon l'ordre de déclaration (premier = 0). Animal.CHIEN.ordinal() /* -> 2 */ name() Obtenir le nom de la valeur. Animal.CHAT.name() /* -> "CHAT" */ valueOf(String s) (méthode statique) Obtenir la valeur dont le nom est spécifié en paramètre. Animal.valueOf("CHAT") /* -> Animal.CHAT */ values() (méthode statique) Obtenir un tableau contenant toutes les valeurs déclarées. Voir les ensembles.
Seven micro-services architecture advantages | Art of Software Engineering Recently I had a conversation with an ops engineer on my team about why micro-service architecture makes a lot of sense and why we should embrace it. Most of our applications are monoliths built over years and having new, small micro-services is something new for our environment. “The term “Microservice Architecture” has sprung up over the last few years to describe a particular way of designing software applications as suites of independently deployable services. While there is no precise definition of this architectural style, there are certain common characteristics around organization around business capability, automated deployment, intelligence in the endpoints, and decentralized control of languages and data.”- Martin Fowler. So here are the points that should help convince your coworkers or boss about micro-services style of SOA: Small, easy to understand code base. Do you see any other advantages that I missed?
How "Good" is Your Data Model?: Validating a Database Design to Pass the Test of Time - Safari Blog by Steve Hoberman Steve Hoberman is the author of the recently released “Data Model Scorecard: Applying the Industry Standard on Data Model Quality” which is published by Technics Publications. He also teaches the “Data Modeling Master Class” which is a leading data modeling course in the industry. He is the founder of the Design Challenges group, Conference Chair of the Data Modeling Zone conference, and recipient of the 2012 Data Administration Management Association (DAMA) International Professional Achievement Award. For many years I have been reviewing data models. A data model is a diagram (along with supporting documentation) that describes business terms (such as Order and Product) and the relationships between these terms (such as a Product may appear on many Orders). I created the Data Model Scorecard to proactively determine how “good” a data model, and therefore improve the structures before the database and code work have begun. To Learn More About Data Model Scorecards
SQL vs NoSQL: The Differences SQL (Structured Query Language) databases have been a primary data storage mechanism for more than four decades. Usage exploded in the late 1990s with the rise of web applications and open-source options such as MySQL, PostgreSQL and SQLite. NoSQL databases have existed since the 1960s, but have been recently gaining traction with popular options such as MongoDB, CouchDB, Redis and Apache Cassandra. You’ll find many tutorials explaining how to use a particular flavor of SQL or NoSQL, but few discuss why you should choose one in preference to the other. Most examples apply to the popular MySQL SQL and MongoDB NoSQL database systems. The SQL vs NoSQL Holy War Before we go further, let’s dispel a number of myths … MYTH: NoSQL supersedes SQL That would be like saying boats were superseded by cars because they’re a newer technology. MYTH: NoSQL is better / worse than SQL Some projects are better suited to using an SQL database. (That said, don’t make life purposely difficult for yourself!
Why the NoSQL Movement Should Have Been Called the JSON Movement By Andy Palmer, Co-founder and CEO of Tamr We all know by now that most “big data” challenges can be characterized into one of the “3 V’s”: Volume: You have too much data Velocity: It is coming at you too fast Variety: It is coming at you from too many places/silos Over the past 5 years, my partner Mike Stonebraker and I have been on a mission to raise awareness of the problems associated with Variety in enterprise data. We started by doing some research at MIT/CSAIL, working closely with Trifacta co-founder Joe Hellerstein. Now that we’re three years into Tamr’s commercial efforts, it’s worth sharing a few thoughts about the state of enterprise data and the potential for large enterprises to compete on integrated analytics. Let me describe why investment in these areas supports the long-term value of enterprise data and will help companies compete on integrated analytics in a sustainable way: I. The result? To realize the analytical potential of the enterprise, systems instead need: II.