OpenReports is a powerful, flexible, and easy to use web reporting solution that provides browser based, parameter driven, dynamic report generation and flexible report scheduling capabilities.
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This project is dedicated to open source data quality and data management initiatives. Data Quality includes profiling, filtering, governance, similarity check, data enrichment/alteration, real time alerting, basket analysis, bubble chart Warehouse validation, single customer view etc.
Mickael Ruau's insight:
FeaturesBig data support - HIVE thrift server supportFormat Creation, Format Matching ( Phone, Date, String and Number), Format standardizationFuzzy Logic based similarity check, Cardinailty check between tables and filesExport and import from XML, XLS or CSV format, PDF exportFile Analysis, Regex search, Standardization, DB searchComplete DB Scan, SQL interface, Data Dictionary, Schema ComparisonStatistical Analysis, Reporting ( dimension and measure based)Pattern Matching , DeDuplication, Case matching, Basket Analysis, Distribution ChartData generation and Data masking featuresMeta Data Information, Reverse engineering of Data ModelTimeliness analysis , String length analysisMysql, Oracle,Postgres,MS Access, Db2, SQL Server certified
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Today I will tell you how to install phpmyadmin on Nginx web server. phpmyadmin is a graphical tool to help you with the administration of MySQL servers. It's
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Un cluster Hadoop adossé à une compétence interne en informatique et statistiques permet de faire ses premiers pas dans le Big Data. Un budget de 100 000 euros par an est à prévoir.
Mickael Ruau's insight:
Un projet de Big Data ne se résume cependant pas à un cluster Hadoop. Des briques complémentaires, open source, pourront venir s'y greffer comme par exemple Hive (datawarehouse), Pig (analyse de données), HDFS (système distribué de fichiers), Storm (analyse de flux)... Mais bien d'autres sont disponibles également (lire l'article : Panorama des solutions de big data). Surtout, un projet Big Data mené en interne nécessite des compétences humaines. Là encore, pas besoin cependant de se doter d'une armée de statisticiens pour commencer.
"Dans une optique de démarrage, on peut s'appuyer sur un informaticien pas forcément capé et doté d'une solide expérience, mais qui se montrera curieux et capable de mettre les mains dans le cambouis", indique Mathias Herberts. "On aura également beaucoup plus de facilité à faire glisser un informaticien sur des compétences de statisticien que l'inverse".
Le coût pour ce type de profil en termes de salaire moyen non chargé tournerait autour de 50 000 euros par an, ce qui fait grimper le budget annuel d'un projet Big Data de départ (comprenant par exemple un cluster Hadoop) à près de 100 000 euros. Quant aux plus gros projets, basés cette fois sur des solutions propriétaires, il n'est pas rare de les voir dépasser les 5 millions d'euros.
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Do these situations sound familiar? You find errors in a production database. You report them. Two months later, the errors are still present.You report an error in a production database. The database staff promptly corrects it that night. Then the problem is back again the following Monday.A long-awaited application finally comes on-line, in a carefully-coordinated weekend update that synchronizes software, database, and security configurations. Three existing applications break because of an unanalyzed change in a database schema.IT announces that a weekend migration of a database to new hardware was successful — but a dozen existing applications lose their access to the data on which they depend.Application developers program to a data dictionary. It clearly documents that every invoice number references a unique customer, or repair authorizations will always be countersigned by officers from two different units, or so on – but they’re not.You’re not alone. As common — and maddening! — as such problems are, though, the good news is that there are well-established ways to attack them.
Mickael Ruau's insight:
These four programming techniques can improve your database quality (and incidentally improve relations between software developers and the data management people). Common sense says that databases deserve to be managed just as other components of a computing solution are. Development and IT staff have strong traditions about how to maintain such “computer stuff” as applications, servers, printer drivers, and so on. When it comes to databases, though, organizations often behave as though all we’ve learned about quality, security, and engineering standards doesn’t apply. For a variety of reasons, databases often suffer undeserved neglect, with predictable consequences for reliability. A few simple changes can heal the worst of the wounds.
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MySQL’s founder is encouraging people to steer away from his creation. Here’s why he’s wrong.
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The MySQL GROUP_CONCAT function isn't a well-known feature, but it is one of the most useful functions that exists in MySQL. I call it the "loop-killer". Here are a few ways to put it to use on your site.
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Percona channels Robin Hood, pilfers Oracle MySQL 5.6 tech
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Des campagnes e-mails qui atteignent les boîtes de réception de vos contacts, voilà l’objectif de chacun d’entre nous. Pourtant, près d’un message e-mail sur quatre n’arrive pas dans les boîtes...
Mickael Ruau's insight:
Afin d’assurer une bonne délivrabilité de vos messages e-mails en boîtes de réception et conserver une bonne réputation auprès des FAI, il est en effet nécessaire de nettoyer régulièrement votre fichier de contacts (suppression des e-NPAI, spam traps, hardbounces…).
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To learn more about the Drizzle project, you can read Brian Aker's reflections of the 3 years leading into the Drizzle 7 release. The Drizzle Documentation is a good read too, and has sections such as A Brief History of Drizzle andNotable MySQL Differences.
Mickael Ruau's insight:
Charter
o A database optimized for Cloud infrastructure and Web applications o Re-designed modular architecture providing plugins with defined APIs
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DB-Engines is an initiative to collect and present information on relational and NoSQL database management systems (DBMS)
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In MySQL 5.6 InnoDB has a dedicated thread (page_cleaner) that’s responsible for performing flushing operations.
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DbSetup from Ninja Squad : a free, open-source Java API that helps you setup your database data to execute DAO/Repository unit tests. No XML (code only), fast, and without any dependencies
Mickael Ruau's insight:
DbSetup is full Java. No XML or YAML file to create. No SQL script. The test data is one control-click away from the source of your test.
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DataVision is a reporting tool similar to Crystal Reports. DV supports many data sources (JDBC, files) and many output formats (HTML, XML, PDF, LaTeX, Excel, delimited files, DocBook). DV includes a GUI editor. DV is embeddable. Reports are XML-based.
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At a glance, MySQL 5.6 is simply a better MySQL with improvements that enhance every functional area of the database kernel, including: Better Performance and ScalabilityImproved InnoDB storage engine for better transactional throughputImproved Optimizer for better query execution times and diagnosticsBetter Application Availability with Online DDL/Schema changesBetter Developer Agility with NoSQL Access with Memcached API to InnoDBImproved Replication for high performance, self-healing distributed deploymentsImproved Performance Schema for better instrumentationImproved Security for worry-free application deploymentsAnd other Important Enhancements
This article serves as a DBA and Developer guide to MySQL 5.6 as it highlights the key new features in each of these areas, many with practical use case examples.
Mickael Ruau's insight:
MySQL 5.6 provides simple, key-value interaction with InnoDB data via the familiar Memcached API. Implemented via a new Memcached daemon plug-in to mysqld, the new Memcached protocol is mapped directly to the native InnoDB API and enables developers to use existing Memcached clients to bypass the expense of query parsing and go directly to InnoDB data for lookups and transactional compliant updates. The API makes it possible to re-use standard Memcached libraries and clients, while extending Memcached functionality by integrating a persistent, crash-safe, transactional database back-end.
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A couple weeks ago Brian Krebs announced that Adobe had a serious breach, of customer data as well as source code for a number of its software products.
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Dear Community,
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MySQL is still the most popular open-source database, but it has been losing fans over the years – for good reason. We look at five practical motivations to dump the MySQL database.
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One of the great things of mysqlnd as a base library for PHP's MySQL support are the collected statistics. mysqlnd collects about 160 different statistical values about all the things going on.
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Having been in the social sciences for a couple of weeks it seems like a large amount of quantitative analysis relies on Principal Component Analysis (PCA). This is usually referred to in tandem with eigenvalues, eigenvectors and lots of numbers. So what’s going on? Is this just mathematical jargon to get the non-maths scholars to stop asking questions? Maybe, but it’s also a useful tool to use when you have to look at data. This post will give a very broad overview of PCA, describing eigenvectors and eigenvalues (which you need to know about to understand it) and showing how you can reduce the dimensions of data using PCA. As I said it’s a neat tool to use in information theory, and even though the maths is a bit complicated, you only need to get a broad idea of what’s going on to be able to use it effectively.
Mickael Ruau's insight:
Example: the OxIS 2013 report
The OxIS 2013 report asked around 2000 people a set of questions about their internet use. It then identified 4 principal components in the data. This is an example of dimension reduction. Let’s say they asked each person 50 questions. There are therefore 50 variables, making it a 50-dimension data set. There will then be 50 eigenvectors/values that will come out of that data set. Let’s say the eigenvalues of that data set were (in descending order): 50, 29, 17, 10, 2, 1, 1, 0.4, 0.2….. There are lots of eigenvalues, but there are only 4 which have big values – indicating along those four directions there is alot of information. These are then identified as the four principal components of the data set (which in the report were labelled as enjoyable escape, instrumental efficiency, social facilitator and problem generator), the data set can then be reduced from 50 dimensions to only 4 by ignoring all the eigenvectors that have insignificant eigenvalues. 4 dimensions is much easier to work with than 50! So dimension reduction using PCA helped simplify this data set by finding the dominant dimensions within it.
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This post comes from Alexey Stroganov at the MySQL Performance Blog. In MySQL 5.6, InnoDB has a dedicated thread (page_cleaner) that’s responsible for performing flushing operations.
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Capture all changes to an application state as a sequence of events. We can query an application's state to find out the current state of the world, and this answers many questions. However there are times when we don't just want to see where we are, we also want to know how we got there.
Event Sourcing ensures that all changes to application state are stored as a sequence of events. Not just can we query these events, we can also use the event log to reconstruct past states, and as a foundation to automatically adjust the state to cope with retroactive changes.
Mickael Ruau's insight:
The simplest way to think of using Event Sourcing is to calculate a requested application state by starting from a blank application state and then applying the events to reach the desired state. It's equally simple to see why this is a slow process, particularly if there are many events.
In many applications it's more common to request recent application states, if so a faster alternative is to store the current application state and if someone wants the special features that Event Sourcing offers then that additional capability is built on top.
Application states can be stored either in memory or on disk. Since an application state is purely derivable from the event log, you can cache it anywhere you like. A system in use during a working day could be started at the beginning of the day from an overnight snapshot and hold the current application state in memory. Should it crash it replays the events from the overnight store. At the end of the working day a new snapshot can be made of the state. New snapshots can be made at any time in parallel without bringing down the running application.
The official system of record can either be the event logs or the current application state. If the current application state is held in a database, then the event logs may only be there for audit and special processing. Alternatively the event logs can be the official record and databases can be built from them whenever needed.
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Unlike other opensource DBMS vendors, CUBRID has no commercial license but only an opensource license in order to provide excellent cost savings to companies. Because of this unique license policy, customers are not required to open the source code of applications or to purchase commercial license.
CUBRID has a separate license for server engine and interface. The server engine adopts the GPL v2 or later license, which allows distribution, modification, and acquisition of source. The interface and tools has the BSD license in which there is no obligation of opening derivative works.
The reason of adopting two separate license systems is that we do not want to give any limitations to independent software vendors (ISV) for developing and distributing various CUBRID based applications.
Mickael Ruau's insight:
Full text of each license can be found at the following link:
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If you install a MySQL Database Server with the default options, your data are insecure and your server is in risk of invasion and some performance Via Linuxaria
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Une des grandes idées des architectures orientées data (Buzz words : OpenData, BigData) est de laisser la possibilité de poser des questions, inconnues aujourd’hui mais qui pourront s’exécuter demain sur les données d’aujourd’hui. Pour cela il suffit “simplement” de stocker lesdonnées plutôt qu’un état (calculé à partir des données).
Mickael Ruau's insight:
En tant que professionnel, il du plus mauvais effet de devoir dire à un client qu’on a perdu des informations, à cause d’un système basé sur des updates. Une application basique se contente souvent de n’avoir que des speed layers et des vues. Lassé de paraître ridicule quand on nous demande ce qu’il se passe sur notre système, on y ajoute un système de log déstructuré qui est souvent insuffisant. Aussi, les bugs corrompent les données et il est parfois impossible de les restaurer. Finalement, mettre une place une architecture orienté data est plus couteuse qu’une application basique mais elle rend obsolète le besoin de logguer et permet de faire infiniement plus de choses, comme la restauration du système lorsqu’un bug a corrompu les vues, et la possibilité d’ajouter des fonctionnalités d’historique qui fonctionneront sur les données du passé. Pour creuser le sujet, je vous conseille de lire l’excellent livre : Big Data
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