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You are an expert in your field, you work with data, and you must crunch numbers to make decisions. You may not have technical skills and resources available to build predictive models. You use spreadsheets or statistical and business intelligence tools to extract meaning from data.
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Mickael Ruau's insight:
Free EditionUse TADA forever, for free
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Hang around long enough anywhere and you’ll pick up familiar nomenclature, general truths, and knowledge. Anyone who has worked with data will nod their head at these phrases.
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This site is comprised of several hundreds of pages that establish a series of online catalogs of design patterns, technology mechanisms, metrics and tool definitions published in support of the following Arcitura certification programs:
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Knowing the formats, types, and usages of data can be more than just good conversation fodder at your company’s next outing. It empowers you with the kinds of information available at your fingertips.
Mickael Ruau's insight:
Five Types of DataString DataA string represents alphanumeric data and can include letters, numbers, spaces, or other types of characters. A string can also be thought of as plain text. All the characters in a string are considered text even if the characters are digits. Numeric Data There are several different numeric data types, including integers, decimals, floats, and doubles. Numeric data types do not have adjustable lengths except for Fixed Decimal. Date/Time Data Date and time data is what it sounds like, though its format can look a bit different. You may have a 10-character String in “yyyy-mm-dd” format for a date or an 8-character String in “hh:mm:ss” format for time. Or, you may have DateTime information that looks something like a 19-character String in “yyyy-mm-dd hh:mm:ss” format. Example: December 2, 2005 = 2005-12-02, 2:47 and 53 seconds a.m. = 02:47:53, 2005-12-02 14:47:53 Boolean Data To start, it’s helpful to know that Bool is an expression with only two possible values. Example: True or False where False equals 0 and True equals non-zero. Spatial Objects The spatial object associated with a data record. There can be multiple spatial object fields contained within a table. Example: A spatial object can consist of a point, line, polyline, or polygon.
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How to extract and interpret data from Trello so that it can be loaded into the analysis tool Grafana and analyzed. Once Trello data is available in Grafana, we provide instructions for building custom reports based on that data and sharing them throughout your organization.
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Via Rémy TESTON, Lionel Reichardt / le Pharmageek
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There are a lot of decisions to make when creating new tables and data warehouses. Some that seem inconsequential at the time end up causing you and your users pain for the life of the database.
Mickael Ruau's insight:
Don’t use dots, spaces, or dashes in database, schema, table, or column names. Dots are for identifying objects, usually in the database.schema.table.column pattern.
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� A practical approach to machine learning.�
Mickael Ruau's insight:
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Have you ever been in a meeting where two sides have two different numbers on the same metric? Any insights from this metrics are forgotten and the fight to have the right number will start.
Mickael Ruau's insight:
Providing trustworthy data sources, building pipelines so others don’t need to repeat calculations, and providing a unique source of truth, that is basically the main job of a data engineer.
But… Which pipelines should we build? What are the most relevant metrics for the business? Which dimensions do we need to break them trough?
Agreeing on naming, metrics and dimensions is a complicated job. We trust our Analyst colleagues to find and converge on the best definitions.
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Apache Kylin is a distributed OLAP engine on Hadoop, which provides sub-second level query latency over datasets scaling to petabytes. Kylin’s superior query performance relies on pre-calculated multi-dimension Cube, which is often time-consuming to build.
Mickael Ruau's insight:
By default, Kylin uses MapReduce Cube Engine built atop of Hadoop MapReduce framework to aggregate huge amounts of source data.
The MR Engine has been well-tuned over years and proven to be stable in hundreds of production deployments. Recently, the Kylin team is trying to further speed up the process of cube building by replacing MR with Spark.
Kyligence has initiated the new Spark Cube Engine with some benchmarks between Spark and MR over different datasets, and has received some promising results. Hear about their results and experiences on moving Cube building, which is a huge computing task, to Spark.
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Ce document d’analyse technique vise à alerter d’une menace informatique ciblant les prestataires de service et les bureaux d’études, ainsi que leurs clients. Des attaquants semblent vouloir prendre position sur les réseaux de prestataires afin de récupérer les données, voire d’accéder aux réseaux, de leurs clients. Les informations contenues dans ce rapport sont issues des analyses de l’ANSSI suite au traitement d’incidents suivant ce schéma d’attaque.
Les premières analyses menées pourraient laisser penser à des attaques en deux phases : elles sont temporellement éloignées et aucun lien technique n’a pour le moment été établi entre les deux. La première phase utilise principalement le code malveillant PlugX. La seconde s’appuie essentiellement sur des outils légitimes et le vol d’identifiants de connexion. Ce document se concentre sur la seconde intrusion et le mode opératoire d’attaque associé : compromission initiale, élévation des privilèges, outils et codes malveillants, latéralisation et objectifs opérationnels. Il présente également les recommandations techniques et organisationnelles à suivre pour permettre aux acteurs ciblés de se protéger.
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L’attaque de « cryptolocker » qui a touché l’ESN cachait-elle plus ? Certains experts l’affirment, même s’ils sont contredits par l’intéressé. Mais la menace sur les prestataires de services apparaît réelle. De même que la possibilité que certains ransomwares ne soient que des écrans de fumée.
Mickael Ruau's insight:
C’était fin janvier : Altran se déclarait victime d’un ransomware. Dans la foulée, Airbus se disait lui-même victime d’un « incident » de sécurité informatique. Il n’a pas fallu bien longtemps pour que certains experts soupçonnent le premier épisode d’être lié au second – mais cet avis est toutefois loin de faire l’unanimité dans la communauté de la sécurité informatique, compte tenu des spécificités du groupe auquel est attribué, à ce jour, le rançongiciel ayant affecté Altran. Deux experts du renseignement sur les menaces nous ont toutefois indiqué, sous couvert d’anonymat, que des données auraient été exfiltrées du système d’information d’Altran et que le ransomware n’aurait été qu’un écran de fumée. De son côté, Altran affirme fermement le contraire et assure « qu’aucune donnée de nos clients n’a été perdue ou volée » à l’occasion de l’incident. Et de faire état d’une grande prudence dans la remise en route graduelle de ses applications et services, mais « nous sommes vraiment très très proches de la fin ». Pour l’entreprise de services numériques (ESN), c’est par mesure de précaution que ses systèmes ont été arrêtés et déconnectés. Cela s’est avéré payant, car cet arrêt a permis d'éviter toute contagion à des systèmes d’information de clients.
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Le Big Data est à la mode et je commencerai cet article par lister les freins
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The DataOps Manifesto for agile, devops and lean in AI, data science, data engineering, data management, big data, business intelligence
Mickael Ruau's insight:
Whether referred to as data science, data engineering, data management, big data, business intelligence, or the like, through our work we have come to value in analytics: Individuals and interactions over processes and tools DataOps Principles1. Continually satisfy your customer:Our highest priority is to satisfy the customer through the early and continuous delivery of valuable analytic insights from a couple of minutes to weeks. 2. Value working analytics:We believe the primary measure of data analytics performance is the degree to which insightful analytics are delivered, incorporating accurate data, atop robust frameworks and systems. 3. Embrace change:We welcome evolving customer needs, and in fact, we embrace them to generate competitive advantage. We believe that the most efficient, effective, and agile method of communication with customers is face-to-face conversation. 4. It's a team sport:Analytic teams will always have a variety of roles, skills, favorite tools, and titles. 5. Daily interactions:Customers, analytic teams, and operations must work together daily throughout the project. 6. Self-organize:We believe that the best analytic insight, algorithms, architectures, requirements, and designs emerge from self-organizing teams. 7. Reduce heroism:As the pace and breadth of need for analytic insights ever increases, we believe analytic teams should strive to reduce heroism and create sustainable and scalable data analytic teams and processes. 8. Reflect:Analytic teams should fine-tune their operational performance by self-reflecting, at regular intervals, on feedback provided by their customers, themselves, and operational statistics. 9. Analytics is code:Analytic teams use a variety of individual tools to access, integrate, model, and visualize data. Fundamentally, each of these tools generates code and configuration which describes the actions taken upon data to deliver insight. 10. Orchestrate:The beginning-to-end orchestration of data, tools, code, environments, and the analytic teams work is a key driver of analytic success. 11. Make it reproducible:Reproducible results are required and therefore we version everything: data, low-level hardware and software configurations, and the code and configuration specific to each tool in the toolchain. 12. Disposable environments:We believe it is important to minimize the cost for analytic team members to experiment by giving them easy to create, isolated, safe, and disposable technical environments that reflect their production environment. 13. Simplicity:We believe that continuous attention to technical excellence and good design enhances agility; likewise simplicity--the art of maximizing the amount of work not done--is essential. 14. Analytics is manufacturing:Analytic pipelines are analogous to lean manufacturing lines. We believe a fundamental concept of DataOps is a focus on process-thinking aimed at achieving continuous efficiencies in the manufacture of analytic insight. 15. Quality is paramount:Analytic pipelines should be built with a foundation capable of automated detection of abnormalities (jidoka) and security issues in code, configuration, and data, and should provide continuous feedback to operators for error avoidance (poka yoke). 16. Monitor quality and performance:Our goal is to have performance, security and quality measures that are monitored continuously to detect unexpected variation and generate operational statistics. 17. Reuse:We believe a foundational aspect of analytic insight manufacturing efficiency is to avoid the repetition of previous work by the individual or team. 18. Improve cycle times:We should strive to minimize the time and effort to turn a customer need into an analytic idea, create it in development, release it as a repeatable production process, and finally refactor and reuse that product.
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MySQL 5.7 Data Types Cheat Sheet from beeftornado. Data types, storage requirements, and max values updated for mysql 5.7
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Data sink is a term used to describe a computer or any other medium capable of receiving data.
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 Sink (computing)This article Please help only describes one highly specialized aspect of its associated subject. improve this article by adding more general information. The talk page may contain suggestions. In computing, a sink, event sink or data sink is a class or function designed to receive incoming events from another object or function. In computing, a sink, event sink or data sink is a class or function designed to receive incoming events from another object or function. This is commonly implemented in C++ as callbacks. Other Object-oriented languages, such as Java and C#, have built-in support for sinks by allowing events to be fired to delegate functions.
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Si vous êtes dans un processus de mise en place d'un nouveau site, de refonte d'un site existant, ou si vous êtes confronté à la fin de Google Search Appliance, quel outil choisir ? Quels sont les critères à privilégier ? Coût, facilité d'intégration, pertinence ? Deux solutions sont notamment en lice pour votre moteur de recherche interne : Elastic Search, et SolR. Quels sont les origines, avantages, et inconvénients de chacune ? Voici quelques éléments de réponse argumentés. N'hésitez pas non plus à aller consulter notre article sur les solutions Saas de recherche, qui offrent aussi bien des réponses intéressantes.
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A tour a various techniques to remove Personally Identifiable Informations (PII) from a Postgres database
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En un mot, le Data Analyst prépare les données et peut en tirer des premières conclusions statistiques, le Data Scientist fait une analyse plus poussée et peut établir des prédictions précieuses, tandis que le Data Architect fait tourner la tuyauterie informatique indispensable pour pouvoir traiter toutes ces données !
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Dans de nombreux secteurs, les data scientists ont besoin de traiter de larges flux de données en temps réel pour fournir des résultats quasi immédiats.
Nous pouvons citer la détection de fraude, le suivi de titres boursiers, la détection d’anomalies sur les chaines industrielles, le suivi du trafic routier et aérien ou encore les systèmes de recommandation.
Le modèle MapReduce est clairement inadapté pour répondre à ces besoins, et les systèmes de traitement à grande échelle comme Spark ou Flink proposent des technologies de traitements de flux massifs en temps réels.
Flink en particulier a nativement été conçu pour le data streaming, et l’application d’opérateurs de fouille de données à des flux. C’est donc ce système qui est présenté dans ce chapitre, et illustré par une application de traitement de messages Twitter en temps réel.
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The primary difference between the computation models of Spark SQL and Spark Core is the relational framework for ingesting, querying and persisting (semi)structured data using relational queries (aka structured queries) that can be expressed in good ol' SQL (with many features of HiveQL) and the high-level SQL-like functional declarative Dataset API (aka Structured Query DSL).
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I think it is safe to state that hash tables (e.g. maps in Go, dicts in Python, HashMap in Java, etc.) are far more common than ordered data structures such as trees for in-memory data structures. One data point is that in a talk about C++ hash table optimization at Google (available in Abseil), the speaker noted that 1% of CPU and 4% of RAM globally at Google is used by hash tables. However, in databases, the default is almost always an ordered index, typically a B-Tree. Why is the "default" choice different between programs and databases, when at the end of the day they both do the same thing: accessing data for our code? More than a year ago I asked about this out loud on Twitter, and got many interesting answers. This is my very delayed attempt to distill what I've learned.
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Paying attention to every link in your supply chain is vital to protect your assets from supply chain attacks.
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