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  • HTML5 data-* (custom data attribute)

    - by Renso
    Goal: Store custom data with the data attribute on any DOM element and retrieve it. Previously under HTML4 we used to use classes to store custom data, something to the affect of <input class="account void limit-5000 over-4999" /> and then have to parse the data out of the class In a book published by Peter-Paul Koch in 2007, ppk on JavaScript, he explains why and how to use custom attributes to make data more accessible to JavaScript, using name-value pairs. Accessing a custom attribute account-limit=5000 is much easier and more intuitive than trying to parse it out of a class, Plus, what if the class name for example "color-5" has a representative class definition in a CSS stylesheet that hides it away or worse some JavaScript plugin that automatically adds 5000 to it, or something crazy like that, just because it is a valid class name. As you can see there are quite a few reasons why using classes is a bad design and why it was important to define custom data attributes in HTML5. Syntax: You define the data attribute by simply prefixing any data item you want to store with any HTML element with "data-". For example to store our customers account data with a hidden input element: <input type="hidden" data-account="void" data-limit=5000 data-over=4999  /> How to access the data: account  -     element.dataset.account limit    -     element.dataset.limit You can also access it by using the more traditional get/setAttribute method or if using jQuery $('#element').attr('data-account','void') Browser support: All except for IE. There is an IE hack around this at http://gist.github.com/362081. Special Note: Be AWARE, do not use upper-case when defining your data elements as it is all converted to lower-case when reading it, so: data-myAccount="A1234" will not be found when you read it with: element.dataset.myAccount Use only lowercase when reading so this will work: element.dataset.myaccount

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  • Master Data Management – A Foundation for Big Data Analysis

    - by Manouj Tahiliani
    While Master Data Management has crossed the proverbial chasm and is on its way to becoming mainstream, businesses are being hammered by a new megatrend called Big Data. Big Data is characterized by massive volumes, its high frequency, the variety of less structured data sources such as email, sensors, smart meters, social networks, and Weblogs, and the need to analyze vast amounts of data to determine value to improve upon management decisions. Businesses that have embraced MDM to get a single, enriched and unified view of Master data by resolving semantic discrepancies and augmenting the explicit master data information from within the enterprise with implicit data from outside the enterprise like social profiles will have a leg up in embracing Big Data solutions. This is especially true for large and medium-sized businesses in industries like Retail, Communications, Financial Services, etc that would find it very challenging to get comprehensive analytical coverage and derive long-term success without resolving the limitations of the heterogeneous topology that leads to disparate, fragmented and incomplete master data. For analytical success from Big Data or in other words ROI from Big Data Investments, businesses need to acquire, organize and analyze the deluge of data to make better decisions. There will need to be a coexistence of structured and unstructured data and to maintain a tight link between the two to extract maximum insights. MDM is the catalyst that helps maintain that tight linkage by providing an understanding about the identity, characteristics of Persons, Companies, Products, Suppliers, etc. associated with the Big Data and thereby help accelerate ROI. In my next post I will discuss about patterns for co-existing Big Data Solutions and MDM. Feel free to provide comments and thoughts on above as well as Integration or Architectural patterns.

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  • How to Assure an Effective Data Model

    As a general rule in my opinion the effectiveness of a data model can be directly related to the accuracy and complexity of a project’s requirements. For example there is no need to work on very detailed data models when the details surrounding a specific data model have not been defined or even clarified. Developing data models when the clarity of project requirements is limited tends to introduce designed issues because the proper details to create an effective data model are not even known. One way to avoid this issue is to create data models that correspond to the complexity of the existing project requirements so that when requirements are updated then new data models can be created based any new discoveries regarding requirements on a fine grain level.  This allows for data models to be composed of general entities to be created initially when a project’s requirements are very vague and then the entities are refined as new and more substantial requirements are defined or redefined. This promotes communication amongst all stakeholders within a project as they go through the process of defining and finalizing project requirements.In addition, here are some general tips that can be applied to projects in regards to data modeling.Initially model all data generally and slowly reactor the data model as new requirements and business constraints are applied to a project.Ensure that data modelers have the proper tools and training they need to design a data model accurately.Create a common location for all project documents so that everyone will be able to review a project’s data models along with any other project documentation.All data models should follow a clear naming schema that tells readers the intended purpose for the data and how it is going to be applied within a project.

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  • SQL SERVER – Why Do We Need Data Quality Services – Importance and Significance of Data Quality Services (DQS)

    - by pinaldave
    Databases are awesome.  I’m sure my readers know my opinion about this – I have made SQL Server my life’s work after all!  I love technology and all things computer-related.  Of course, even with my love for technology, I have to admit that it has its limits.  For example, it takes a human brain to notice that data has been input incorrectly.  Computer “brains” might be faster than humans, but human brains are still better at pattern recognition.  For example, a human brain will notice that “300” is a ridiculous age for a human to be, but to a computer it is just a number.  A human will also notice similarities between “P. Dave” and “Pinal Dave,” but this would stump most computers. In a database, these sorts of anomalies are incredibly important.  Databases are often used by multiple people who rely on this data to be true and accurate, so data quality is key.  That is why the improved SQL Server features Master Data Management talks about Data Quality Services.  This service has the ability to recognize and flag anomalies like out of range numbers and similarities between data.  This allows a human brain with its pattern recognition abilities to double-check and ensure that P. Dave is the same as Pinal Dave. A nice feature of Data Quality Services is that once you set the rules for the program to follow, it will not only keep your data organized in the future, but go to the past and “fix up” any data that has already been entered.  It also allows you do combine data from multiple places and it will apply these rules across the board, so that you don’t have any weird issues that crop up when trying to fit a round peg into a square hole. There are two parts of Data Quality Services that help you accomplish all these neat things.  The first part is DQL Server, which you can think of as the hardware component of the system.  It is installed on the side of (it needs to install separately after SQL Server is installed) SQL Server and runs quietly in the background, performing all its cleanup services. DQS Client is the user interface that you can interact with to set the rules and check over your data.  There are three main aspects of Client: knowledge base management, data quality projects and administration.  Knowledge base management is the part of the system that allows you to set the rules, or program the “knowledge base,” so that your database is clean and consistent. Data Quality projects are what run in the background and clean up the data that is already present.  The administration allows you to check out what DQS Client is doing, change rules, and generally oversee the entire process.  The whole process is user-friendly and a pleasure to use.  I highly recommend implementing Data Quality Services in your database. Here are few of my blog posts which are related to Data Quality Services and I encourage you to try this out. SQL SERVER – Installing Data Quality Services (DQS) on SQL Server 2012 SQL SERVER – Step by Step Guide to Beginning Data Quality Services in SQL Server 2012 – Introduction to DQS SQL SERVER – DQS Error – Cannot connect to server – A .NET Framework error occurred during execution of user-defined routine or aggregate “SetDataQualitySessions” – SetDataQualitySessionPhaseTwo SQL SERVER – Configuring Interactive Cleansing Suggestion Min Score for Suggestions in Data Quality Services (DQS) – Sensitivity of Suggestion SQL SERVER – Unable to DELETE Project in Data Quality Projects (DQS) Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Data Quality Services, DQS

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  • Welcome Oracle Data Integration 12c: Simplified, Future-Ready Solutions with Extreme Performance

    - by Irem Radzik
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 The big day for the Oracle Data Integration team has finally arrived! It is my honor to introduce you to Oracle Data Integration 12c. Today we announced the general availability of 12c release for Oracle’s key data integration products: Oracle Data Integrator 12c and Oracle GoldenGate 12c. The new release delivers extreme performance, increase IT productivity, and simplify deployment, while helping IT organizations to keep pace with new data-oriented technology trends including cloud computing, big data analytics, real-time business intelligence. With the 12c release Oracle becomes the new leader in the data integration and replication technologies as no other vendor offers such a complete set of data integration capabilities for pervasive, continuous access to trusted data across Oracle platforms as well as third-party systems and applications. Oracle Data Integration 12c release addresses data-driven organizations’ critical and evolving data integration requirements under 3 key themes: Future-Ready Solutions Extreme Performance Fast Time-to-Value       There are many new features that support these key differentiators for Oracle Data Integrator 12c and for Oracle GoldenGate 12c. In this first 12c blog post, I will highlight only a few:·Future-Ready Solutions to Support Current and Emerging Initiatives: Oracle Data Integration offer robust and reliable solutions for key technology trends including cloud computing, big data analytics, real-time business intelligence and continuous data availability. Via the tight integration with Oracle’s database, middleware, and application offerings Oracle Data Integration will continue to support the new features and capabilities right away as these products evolve and provide advance features. E    Extreme Performance: Both GoldenGate and Data Integrator are known for their high performance. The new release widens the gap even further against competition. Oracle GoldenGate 12c’s Integrated Delivery feature enables higher throughput via a special application programming interface into Oracle Database. As mentioned in the press release, customers already report up to 5X higher performance compared to earlier versions of GoldenGate. Oracle Data Integrator 12c introduces parallelism that significantly increases its performance as well. Fast Time-to-Value via Higher IT Productivity and Simplified Solutions:  Oracle Data Integrator 12c’s new flow-based declarative UI brings superior developer productivity, ease of use, and ultimately fast time to market for end users.  It also gives the ability to seamlessly reuse mapping logic speeds development.Oracle GoldenGate 12c ‘s Integrated Delivery feature automatically optimally tunes the process, saving time while improving performance. This is just a quick glimpse into Oracle Data Integrator 12c and Oracle GoldenGate 12c. On November 12th we will reveal much more about the new release in our video webcast "Introducing 12c for Oracle Data Integration". Our customer and partner speakers, including SolarWorld, BT, Rittman Mead will join us in launching the new release. Please join us at this free event to learn more from our executives about the 12c release, hear our customers’ perspectives on the new features, and ask your questions to our experts in the live Q&A. Also, please continue to follow our blogs, tweets, and Facebook updates as we unveil more about the new features of the latest release. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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  • Big Data – Final Wrap and What Next – Day 21 of 21

    - by Pinal Dave
    In yesterday’s blog post we explored various resources related to learning Big Data and in this blog post we will wrap up this 21 day series on Big Data. I have been exploring various terms and technology related to Big Data this entire month. It was indeed fun to write about Big Data in 21 days but the subject of Big Data is much bigger and larger than someone can cover it in 21 days. My first goal was to write about the basics and I think we have got that one covered pretty well. During this 21 days I have received many questions and answers related to Big Data. I have covered a few of the questions in this series and a few more I will be covering in the next coming months. Now after understanding Big Data basics. I am personally going to do a list of the things next. I thought I will share the same with you as this will give you a good idea how to continue the journey of the Big Data. Build a schedule to read various Apache documentations Watch all Pluralsight Courses Explore HortonWorks Sandbox Start building presentation about Big Data – this is a great way to learn something new Present in User Groups Meetings on Big Data Topics Write more blog posts about Big Data I am going to continue learning about Big Data – I want you to continue learning Big Data. Please leave a comment how you are going to continue learning about Big Data. I will publish all the informative comments on this blog with due credit. I want to end this series with the infographic by UMUC. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Partner Webcast - Focus on Oracle Data Profiling and Data Quality 11g

    - by lukasz.romaszewski(at)oracle.com
    Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:RO;} Partner Webcast Focus on Oracle Data Profiling and Data Quality 11g February 24th, 12am  CET   Oracle offers an integrated suite Data Quality software architected to discover and correct today's data quality problems and establish a platform prepared for tomorrow's yet unknown data challenges. Oracle Data Profiling provides data investigation, discovery, and profiling in support of quality, migration, integration, stewardship, and governance initiatives. It includes a broad range of features that expand upon basic profiling, including automated monitoring, business-rule validation, and trend analysis. Oracle Data Quality for Data Integrator provides cleansing, standardization, matching, address validation, location enrichment, and linking functions for global customer data and operational business data. It ensures that data adheres to established standards that are adaptable to fit each organization's specific needs.  Both single - and double - byte data are processed in local languages to provide a unique and centralized view of customers, products and services.   During this in-person briefing, Data Integration Solution Specialists will be providing a technical overview and a walkthrough.   Agenda ·         Oracle Data Integration Strategy overview ·         A focus on Oracle Data Profiling and Oracle Data Quality for Data Integrator: o   Oracle Data Profiling o   Oracle Data Quality for Data Integrator o   Live demoo   Q&A Delivery Format  This FREE online LIVE eSeminar will be delivered over the Web and Conference Call. Registrations   received less than 24hours  prior to start time may not receive confirmation to attend. To register , click here. For any questions please contact [email protected]

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  • operator overloading of stream extraction operator in C++ help

    - by Crystal
    I'm having some trouble overloading my stream extraction operator in C++ for a hw assignment. I'm not really sure why I am getting these compile errors since I thought I was doing it right... Here is my code: Complex.h #ifndef COMPLEX_H #define COMPLEX_H class Complex { //friend ostream &operator<<(ostream &output, const Complex &complexObj) const; public: Complex(double = 0.0, double = 0.0); // constructor Complex operator+(const Complex &) const; // addition Complex operator-(const Complex &) const; // subtraction void print() const; // output private: double real; // real part double imaginary; // imaginary part }; #endif Complex.cpp #include <iostream> #include "Complex.h" using namespace std; // Constructor Complex::Complex(double realPart, double imaginaryPart) : real(realPart), imaginary(imaginaryPart) { } // addition operator Complex Complex::operator+(const Complex &operand2) const { return Complex(real + operand2.real, imaginary + operand2.imaginary); } // subtraction operator Complex Complex::operator-(const Complex &operand2) const { return Complex(real - operand2.real, imaginary - operand2.imaginary); } // Overload << operator ostream &Complex::operator<<(ostream &output, const Complex &complexObj) const { cout << '(' << complexObj.real << ", " << complexObj.imaginary << ')'; return output; // returning output allows chaining } // display a Complex object in the form: (a, b) void Complex::print() const { cout << '(' << real << ", " << imaginary << ')'; } main.cpp #include <iostream> #include "Complex.h" using namespace std; int main() { Complex x; Complex y(4.3, 8.2); Complex z(3.3, 1.1); cout << "x: "; x.print(); cout << "\ny: "; y.print(); cout << "\nz: "; z.print(); x = y + z; cout << "\n\nx = y + z: " << endl; x.print(); cout << " = "; y.print(); cout << " + "; z.print(); x = y - z; cout << "\n\nx = y - z: " << endl; x.print(); cout << " = "; y.print(); cout << " - "; z.print(); cout << endl; } Compile erros: complex.cpp(23) : error C2039: '<<' : is not a member of 'Complex' complex.h(5) : see declaration of 'Complex' complex.cpp(24) : error C2270: '<<' : modifiers not allowed on nonmember functions complex.cpp(25) : error C2248: 'Complex::real' : cannot access private member declared in class 'Complex' complex.h(13) : see declaration of 'Complex::real' complex.h(5) : see declaration of 'Complex' complex.cpp(25) : error C2248: 'Complex::imaginary' : cannot access private member declared in class 'Complex' complex.h(14) : see declaration of 'Complex::imaginary' complex.h(5) : see declaration of 'Complex' Thanks!

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  • Big Data – Buzz Words: What is HDFS – Day 8 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned what is MapReduce. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – HDFS. What is HDFS ? HDFS stands for Hadoop Distributed File System and it is a primary storage system used by Hadoop. It provides high performance access to data across Hadoop clusters. It is usually deployed on low-cost commodity hardware. In commodity hardware deployment server failures are very common. Due to the same reason HDFS is built to have high fault tolerance. The data transfer rate between compute nodes in HDFS is very high, which leads to reduced risk of failure. HDFS creates smaller pieces of the big data and distributes it on different nodes. It also copies each smaller piece to multiple times on different nodes. Hence when any node with the data crashes the system is automatically able to use the data from a different node and continue the process. This is the key feature of the HDFS system. Architecture of HDFS The architecture of the HDFS is master/slave architecture. An HDFS cluster always consists of single NameNode. This single NameNode is a master server and it manages the file system as well regulates access to various files. In additional to NameNode there are multiple DataNodes. There is always one DataNode for each data server. In HDFS a big file is split into one or more blocks and those blocks are stored in a set of DataNodes. The primary task of the NameNode is to open, close or rename files and directory and regulate access to the file system, whereas the primary task of the DataNode is read and write to the file systems. DataNode is also responsible for the creation, deletion or replication of the data based on the instruction from NameNode. In reality, NameNode and DataNode are software designed to run on commodity machine build in Java language. Visual Representation of HDFS Architecture Let us understand how HDFS works with the help of the diagram. Client APP or HDFS Client connects to NameSpace as well as DataNode. Client App access to the DataNode is regulated by NameSpace Node. NameSpace Node allows Client App to connect to the DataNode based by allowing the connection to the DataNode directly. A big data file is divided into multiple data blocks (let us assume that those data chunks are A,B,C and D. Client App will later on write data blocks directly to the DataNode. Client App does not have to directly write to all the node. It just has to write to any one of the node and NameNode will decide on which other DataNode it will have to replicate the data. In our example Client App directly writes to DataNode 1 and detained 3. However, data chunks are automatically replicated to other nodes. All the information like in which DataNode which data block is placed is written back to NameNode. High Availability During Disaster Now as multiple DataNode have same data blocks in the case of any DataNode which faces the disaster, the entire process will continue as other DataNode will assume the role to serve the specific data block which was on the failed node. This system provides very high tolerance to disaster and provides high availability. If you notice there is only single NameNode in our architecture. If that node fails our entire Hadoop Application will stop performing as it is a single node where we store all the metadata. As this node is very critical, it is usually replicated on another clustered as well as on another data rack. Though, that replicated node is not operational in architecture, it has all the necessary data to perform the task of the NameNode in the case of the NameNode fails. The entire Hadoop architecture is built to function smoothly even there are node failures or hardware malfunction. It is built on the simple concept that data is so big it is impossible to have come up with a single piece of the hardware which can manage it properly. We need lots of commodity (cheap) hardware to manage our big data and hardware failure is part of the commodity servers. To reduce the impact of hardware failure Hadoop architecture is built to overcome the limitation of the non-functioning hardware. Tomorrow In tomorrow’s blog post we will discuss the importance of the relational database in Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Entity Association Mapping with Code First Part 1 : Mapping Complex Types

    - by mortezam
    Last week the CTP5 build of the new Entity Framework Code First has been released by data team at Microsoft. Entity Framework Code-First provides a pretty powerful code-centric way to work with the databases. When it comes to associations, it brings ultimate flexibility. I’m a big fan of the EF Code First approach and am planning to explain association mapping with code first in a series of blog posts and this one is dedicated to Complex Types. If you are new to Code First approach, you can find a great walkthrough here. In order to build a solid foundation for our discussion, we will start by learning about some of the core concepts around the relationship mapping.   What is Mapping?Mapping is the act of determining how objects and their relationships are persisted in permanent data storage, in our case, relational databases. What is Relationship mapping?A mapping that describes how to persist a relationship (association, aggregation, or composition) between two or more objects. Types of RelationshipsThere are two categories of object relationships that we need to be concerned with when mapping associations. The first category is based on multiplicity and it includes three types: One-to-one relationships: This is a relationship where the maximums of each of its multiplicities is one. One-to-many relationships: Also known as a many-to-one relationship, this occurs when the maximum of one multiplicity is one and the other is greater than one. Many-to-many relationships: This is a relationship where the maximum of both multiplicities is greater than one. The second category is based on directionality and it contains two types: Uni-directional relationships: when an object knows about the object(s) it is related to but the other object(s) do not know of the original object. To put this in EF terminology, when a navigation property exists only on one of the association ends and not on the both. Bi-directional relationships: When the objects on both end of the relationship know of each other (i.e. a navigation property defined on both ends). How Object Relationships Are Implemented in POCO domain models?When the multiplicity is one (e.g. 0..1 or 1) the relationship is implemented by defining a navigation property that reference the other object (e.g. an Address property on User class). When the multiplicity is many (e.g. 0..*, 1..*) the relationship is implemented via an ICollection of the type of other object. How Relational Database Relationships Are Implemented? Relationships in relational databases are maintained through the use of Foreign Keys. A foreign key is a data attribute(s) that appears in one table and must be the primary key or other candidate key in another table. With a one-to-one relationship the foreign key needs to be implemented by one of the tables. To implement a one-to-many relationship we implement a foreign key from the “one table” to the “many table”. We could also choose to implement a one-to-many relationship via an associative table (aka Join table), effectively making it a many-to-many relationship. Introducing the ModelNow, let's review the model that we are going to use in order to implement Complex Type with Code First. It's a simple object model which consist of two classes: User and Address. Each user could have one billing address. The Address information of a User is modeled as a separate class as you can see in the UML model below: In object-modeling terms, this association is a kind of aggregation—a part-of relationship. Aggregation is a strong form of association; it has some additional semantics with regard to the lifecycle of objects. In this case, we have an even stronger form, composition, where the lifecycle of the part is fully dependent upon the lifecycle of the whole. Fine-grained domain models The motivation behind this design was to achieve Fine-grained domain models. In crude terms, fine-grained means “more classes than tables”. For example, a user may have both a billing address and a home address. In the database, you may have a single User table with the columns BillingStreet, BillingCity, and BillingPostalCode along with HomeStreet, HomeCity, and HomePostalCode. There are good reasons to use this somewhat denormalized relational model (performance, for one). In our object model, we can use the same approach, representing the two addresses as six string-valued properties of the User class. But it’s much better to model this using an Address class, where User has the BillingAddress and HomeAddress properties. This object model achieves improved cohesion and greater code reuse and is more understandable. Complex Types: Splitting a Table Across Multiple Types Back to our model, there is no difference between this composition and other weaker styles of association when it comes to the actual C# implementation. But in the context of ORM, there is a big difference: A composed class is often a candidate Complex Type. But C# has no concept of composition—a class or property can’t be marked as a composition. The only difference is the object identifier: a complex type has no individual identity (i.e. no AddressId defined on Address class) which make sense because when it comes to the database everything is going to be saved into one single table. How to implement a Complex Types with Code First Code First has a concept of Complex Type Discovery that works based on a set of Conventions. The convention is that if Code First discovers a class where a primary key cannot be inferred, and no primary key is registered through Data Annotations or the fluent API, then the type will be automatically registered as a complex type. Complex type detection also requires that the type does not have properties that reference entity types (i.e. all the properties must be scalar types) and is not referenced from a collection property on another type. Here is the implementation: public class User{    public int UserId { get; set; }    public string FirstName { get; set; }    public string LastName { get; set; }    public string Username { get; set; }    public Address Address { get; set; }} public class Address {     public string Street { get; set; }     public string City { get; set; }            public string PostalCode { get; set; }        }public class EntityMappingContext : DbContext {     public DbSet<User> Users { get; set; }        } With code first, this is all of the code we need to write to create a complex type, we do not need to configure any additional database schema mapping information through Data Annotations or the fluent API. Database SchemaThe mapping result for this object model is as follows: Limitations of this mappingThere are two important limitations to classes mapped as Complex Types: Shared references is not possible: The Address Complex Type doesn’t have its own database identity (primary key) and so can’t be referred to by any object other than the containing instance of User (e.g. a Shipping class that also needs to reference the same User Address). No elegant way to represent a null reference There is no elegant way to represent a null reference to an Address. When reading from database, EF Code First always initialize Address object even if values in all mapped columns of the complex type are null. This means that if you store a complex type object with all null property values, EF Code First returns a initialized complex type when the owning entity object is retrieved from the database. SummaryIn this post we learned about fine-grained domain models which complex type is just one example of it. Fine-grained is fully supported by EF Code First and is known as the most important requirement for a rich domain model. Complex type is usually the simplest way to represent one-to-one relationships and because the lifecycle is almost always dependent in such a case, it’s either an aggregation or a composition in UML. In the next posts we will revisit the same domain model and will learn about other ways to map a one-to-one association that does not have the limitations of the complex types. References ADO.NET team blog Mapping Objects to Relational Databases Java Persistence with Hibernate

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  • Oracle Data Integration 12c: Perspectives of Industry Experts, Customers and Partners

    - by Irem Radzik
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 As you may have seen from our recent blog posts on Oracle Data Integrator 12c and Oracle GoldenGate 12c, we are very excited to share with you the great new features the 12c release brings to Oracle’s data integration solutions. And, fortunately we are not alone in this sentiment. Since the press announcement October 17th, which incorporates our customers' and experts' testimonials, we have seen positive comments in leading technology publications and social media as well. Here are some examples: In CIO and PCWorld you can find Joab Jackson’s article, Oracle Data Integrator 12c ready for real-time analysis, where wrote about the tight integration between Oracle Data Integrator and Oracle GoldenGate . He noted “Heeding the call from enterprise customers who clamor for more immediacy in their data-driven reports, Oracle has updated its data-integration software portfolio so that it can more rapidly deliver data to data warehouses and analysis applications.” Integration Developer News’ Vance McCarthy wrote the article Oracle Ships ‘Future Proofs’ Integration Tools for Traditional, Cloud, Big Data, Real-Time Projects and mentioned that “Oracle Data Integrator 12c and Oracle GoldenGate 12c sport a wide range of improvements to let devs more easily deliver data integration for cloud, analytics, big data and other new projects that leverage multiple datasets for business.“ InformationWeek’s Doug Henschen gave a great overview to several key features including the new flow-based UI in Oracle Data Integrator. Doug said “Oracle Data Integrator 12c introduces a complete makeover of the job-building experience, while real-time oriented GoldenGate 12c introduces performance gains “. In Database Trends and Applications’ article Oracle Strengthens Data Integration with Release of Oracle Data Integrator 12c and Oracle GoldenGate 12c highlighted the productivity aspect of the new solution with his remarks: “tight integration between Oracle Data Integrator 12c and Oracle GoldenGate 12c enables developers to leverage Oracle GoldenGate’s low overhead, real-time change data capture completely within the Oracle Data Integrator Studio without additional training”. We are also thrilled about what our customers and partners have to say about our products and the new release. And we are equally excited to share those perspectives with you in our upcoming launch video webcast on November 12th. SolarWorld Industries America’s Senior Database Manager, Russ Toyama will join our executives in our studio in Redwood Shores to discuss GoldenGate’s core benefits and the new release, while Surren Partharb, CTO of Strategic Technology Services for BT, and Mark Rittman, CTO of Rittman Mead, will provide their comments via the interviews conducted in the UK. This interactive panel discussion in the video webcast will unveil the new release with the expertise of our development executives and the great insight from our customers and partners. In addition, our product experts will be available online to answer chat questions. This is really a great opportunity to learn how Oracle's data integration offering has changed the integration and replication technology space with the new release, and established itself as the new leader. If you have not registered for this free event yet, you can do so via this link. We will run the live event at 8am PT/4pm GMT, followed by a replay of the event with live chat for Q&A  at 10am PT/6pm GMT. The replay will be available on-demand for those who register but cannot attend either session on November 12th. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Times New Roman","serif"; mso-fareast-font-family:"Times New Roman";}

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  • iPhone NSXMLParser parsing string and storing in a NSNumber variable and different data types

    - by anubhav
    Hi, I am trying to parse a XML File using NSXMLParser, I also have a Container class, in which I have a few instance variables. One of the elements that I am trying to parse in the XML is: <book sn="32.859669048339128" pn="-116.917800670489670"> I am trying to save the value of sn and pn in an instance variable of object Container: NSNumber *sn, NSNumber *pn. I want it so that when my parser get the attributeValues it can save it as a Double (or float) in those NSNumber pointers. Right now, all it does it just saves a string to the NSNumber. The Parser Code looks like this: if([elementName isEqualToString:@"book"]){ container = [[Container alloc] init]; container.sn=[attributeDict objectForKey:@"sn"]; container.pn=[attributeDict objectForKey:@"pn"]; } I want it so that the type of container.sn is initialized to a float or double. Any ideas how to do this? Thanks in advance!

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  • fluent api complex example

    - by intern
    we have tried some of simple Fluent API examples. to brush up our skills we want to move ahead to complex examples. but we do not what to make as a complex fluent api. can anyone suggest what should we make or where can we get the idea about it? we have recently started writing fluent api codes in Ruby and have tested very basic ones. Now we want to move to complex ones to get better idea about it.

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  • Precision of cos(atan2(y,x)) versus using complex <double>, C++

    - by Ivan
    Hi all, I'm writing some coordinate transformations (more specifically the Joukoswky Transform, Wikipedia Joukowsky Transform), and I'm interested in performance, but of course precision. I'm trying to do the coordinate transformations in two ways: 1) Calculating the real and complex parts in separate, using double precision, as below: double r2 = chi.x*chi.x + chi.y*chi.y; //double sq = pow(r2,-0.5*n) + pow(r2,0.5*n); //slow!!! double sq = sqrt(r2); //way faster! double co = cos(atan2(chi.y,chi.x)); double si = sin(atan2(chi.y,chi.x)); Z.x = 0.5*(co*sq + co/sq); Z.y = 0.5*si*sq; where chi and Z are simple structures with double x and y as members. 2) Using complex : Z = 0.5 * (chi + (1.0 / chi)); Where Z and chi are complex . There interesting part is that indeed the case 1) is faster (about 20%), but the precision is bad, giving error in the third decimal number after the comma after the inverse transform, while the complex gives back the exact number. So, the problem is on the cos(atan2), sin(atan2)? But if it is, how the complex handles that? Thanks!

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  • New Big Data Appliance Security Features

    - by mgubar
    The Oracle Big Data Appliance (BDA) is an engineered system for big data processing.  It greatly simplifies the deployment of an optimized Hadoop Cluster – whether that cluster is used for batch or real-time processing.  The vast majority of BDA customers are integrating the appliance with their Oracle Databases and they have certain expectations – especially around security.  Oracle Database customers have benefited from a rich set of security features:  encryption, redaction, data masking, database firewall, label based access control – and much, much more.  They want similar capabilities with their Hadoop cluster.    Unfortunately, Hadoop wasn’t developed with security in mind.  By default, a Hadoop cluster is insecure – the antithesis of an Oracle Database.  Some critical security features have been implemented – but even those capabilities are arduous to setup and configure.  Oracle believes that a key element of an optimized appliance is that its data should be secure.  Therefore, by default the BDA delivers the “AAA of security”: authentication, authorization and auditing. Security Starts at Authentication A successful security strategy is predicated on strong authentication – for both users and software services.  Consider the default configuration for a newly installed Oracle Database; it’s been a long time since you had a legitimate chance at accessing the database using the credentials “system/manager” or “scott/tiger”.  The default Oracle Database policy is to lock accounts thereby restricting access; administrators must consciously grant access to users. Default Authentication in Hadoop By default, a Hadoop cluster fails the authentication test. For example, it is easy for a malicious user to masquerade as any other user on the system.  Consider the following scenario that illustrates how a user can access any data on a Hadoop cluster by masquerading as a more privileged user.  In our scenario, the Hadoop cluster contains sensitive salary information in the file /user/hrdata/salaries.txt.  When logged in as the hr user, you can see the following files.  Notice, we’re using the Hadoop command line utilities for accessing the data: $ hadoop fs -ls /user/hrdataFound 1 items-rw-r--r--   1 oracle supergroup         70 2013-10-31 10:38 /user/hrdata/salaries.txt$ hadoop fs -cat /user/hrdata/salaries.txtTom Brady,11000000Tom Hanks,5000000Bob Smith,250000Oprah,300000000 User DrEvil has access to the cluster – and can see that there is an interesting folder called “hrdata”.  $ hadoop fs -ls /user Found 1 items drwx------   - hr supergroup          0 2013-10-31 10:38 /user/hrdata However, DrEvil cannot view the contents of the folder due to lack of access privileges: $ hadoop fs -ls /user/hrdata ls: Permission denied: user=drevil, access=READ_EXECUTE, inode="/user/hrdata":oracle:supergroup:drwx------ Accessing this data will not be a problem for DrEvil. He knows that the hr user owns the data by looking at the folder’s ACLs. To overcome this challenge, he will simply masquerade as the hr user. On his local machine, he adds the hr user, assigns that user a password, and then accesses the data on the Hadoop cluster: $ sudo useradd hr $ sudo passwd $ su hr $ hadoop fs -cat /user/hrdata/salaries.txt Tom Brady,11000000 Tom Hanks,5000000 Bob Smith,250000 Oprah,300000000 Hadoop has not authenticated the user; it trusts that the identity that has been presented is indeed the hr user. Therefore, sensitive data has been easily compromised. Clearly, the default security policy is inappropriate and dangerous to many organizations storing critical data in HDFS. Big Data Appliance Provides Secure Authentication The BDA provides secure authentication to the Hadoop cluster by default – preventing the type of masquerading described above. It accomplishes this thru Kerberos integration. Figure 1: Kerberos Integration The Key Distribution Center (KDC) is a server that has two components: an authentication server and a ticket granting service. The authentication server validates the identity of the user and service. Once authenticated, a client must request a ticket from the ticket granting service – allowing it to access the BDA’s NameNode, JobTracker, etc. At installation, you simply point the BDA to an external KDC or automatically install a highly available KDC on the BDA itself. Kerberos will then provide strong authentication for not just the end user – but also for important Hadoop services running on the appliance. You can now guarantee that users are who they claim to be – and rogue services (like fake data nodes) are not added to the system. It is common for organizations to want to leverage existing LDAP servers for common user and group management. Kerberos integrates with LDAP servers – allowing the principals and encryption keys to be stored in the common repository. This simplifies the deployment and administration of the secure environment. Authorize Access to Sensitive Data Kerberos-based authentication ensures secure access to the system and the establishment of a trusted identity – a prerequisite for any authorization scheme. Once this identity is established, you need to authorize access to the data. HDFS will authorize access to files using ACLs with the authorization specification applied using classic Linux-style commands like chmod and chown (e.g. hadoop fs -chown oracle:oracle /user/hrdata changes the ownership of the /user/hrdata folder to oracle). Authorization is applied at the user or group level – utilizing group membership found in the Linux environment (i.e. /etc/group) or in the LDAP server. For SQL-based data stores – like Hive and Impala – finer grained access control is required. Access to databases, tables, columns, etc. must be controlled. And, you want to leverage roles to facilitate administration. Apache Sentry is a new project that delivers fine grained access control; both Cloudera and Oracle are the project’s founding members. Sentry satisfies the following three authorization requirements: Secure Authorization:  the ability to control access to data and/or privileges on data for authenticated users. Fine-Grained Authorization:  the ability to give users access to a subset of the data (e.g. column) in a database Role-Based Authorization:  the ability to create/apply template-based privileges based on functional roles. With Sentry, “all”, “select” or “insert” privileges are granted to an object. The descendants of that object automatically inherit that privilege. A collection of privileges across many objects may be aggregated into a role – and users/groups are then assigned that role. This leads to simplified administration of security across the system. Figure 2: Object Hierarchy – granting a privilege on the database object will be inherited by its tables and views. Sentry is currently used by both Hive and Impala – but it is a framework that other data sources can leverage when offering fine-grained authorization. For example, one can expect Sentry to deliver authorization capabilities to Cloudera Search in the near future. Audit Hadoop Cluster Activity Auditing is a critical component to a secure system and is oftentimes required for SOX, PCI and other regulations. The BDA integrates with Oracle Audit Vault and Database Firewall – tracking different types of activity taking place on the cluster: Figure 3: Monitored Hadoop services. At the lowest level, every operation that accesses data in HDFS is captured. The HDFS audit log identifies the user who accessed the file, the time that file was accessed, the type of access (read, write, delete, list, etc.) and whether or not that file access was successful. The other auditing features include: MapReduce:  correlate the MapReduce job that accessed the file Oozie:  describes who ran what as part of a workflow Hive:  captures changes were made to the Hive metadata The audit data is captured in the Audit Vault Server – which integrates audit activity from a variety of sources, adding databases (Oracle, DB2, SQL Server) and operating systems to activity from the BDA. Figure 4: Consolidated audit data across the enterprise.  Once the data is in the Audit Vault server, you can leverage a rich set of prebuilt and custom reports to monitor all the activity in the enterprise. In addition, alerts may be defined to trigger violations of audit policies. Conclusion Security cannot be considered an afterthought in big data deployments. Across most organizations, Hadoop is managing sensitive data that must be protected; it is not simply crunching publicly available information used for search applications. The BDA provides a strong security foundation – ensuring users are only allowed to view authorized data and that data access is audited in a consolidated framework.

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  • How to present a stable data model in a public API that allows internal data structures to be changed without breaking the public view of the data?

    - by Max Palmer
    I am in the process of developing an application that allows users to write C# scripts. These scripts allow users to call selected methods and to access and manipulate data in a document. This works well, however, in the development version, scripts access the document's (internal) data structures directly. This means that if we were to change the internal data model/structure, there is a good chance that someone's script will no longer compile. We obviously want to prevent this breaking change from happening, but still want to allow the user to write sensible C# code (whilst not restricting how we develop our internal data model as a result). We therefore need to decouple our scripting API and its data structures from our internal methods and data structures. We've a few ideas as to how we might allow the user to access a what is effectively a stable public version of the document's internal data*, but I wanted to throw the question out there to someone who might have some real experience of this problem. NB our internal document's data structure is quite complex and it could be quite difficult to wrap. We know we want to expose as little as possible in our public API, especially as once it's out there, it's out there for good. Can anyone help? How do scripting languages / APIs decouple their public API and data structures from their internal data structures? Is there no real alternative to having to write a complex interaction layer? If we need to do this, what's a good approach or pattern for wrapping complex data structures that include nested objects, including collections? I've looked at the API facade pattern, which looks like it's trying to address these kinds of issues, but are there alternatives? *One idea is to build a data facade that is kept stable across versions of our application. The facade exposes a set of facade data objects that are used in the script code. These maintain backwards compatibility and wrap access to our internal document's data model.

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  • Why Cornell University Chose Oracle Data Masking

    - by Troy Kitch
    One of the eight Ivy League schools, Cornell University found itself in the unfortunate position of having to inform over 45,000 University community members that their personal information had been breached when a laptop was stolen. To ensure this wouldn’t happen again, Cornell took steps to ensure that data used for non-production purposes is de-identified with Oracle Data Masking. A recent podcast highlights why organizations like Cornell are choosing Oracle Data Masking to irreversibly de-identify production data for use in non-production environments. Organizations often copy production data, that contains sensitive information, into non-production environments so they can test applications and systems using “real world” information. Data in non-production has increasingly become a target of cyber criminals and can be lost or stolen due to weak security controls and unmonitored access. Similar to production environments, data breaches in non-production environments can cost millions of dollars to remediate and cause irreparable harm to reputation and brand. Cornell’s applications and databases help carry out the administrative and academic mission of the university. They are running Oracle PeopleSoft Campus Solutions that include highly sensitive faculty, student, alumni, and prospective student data. This data is supported and accessed by a diverse set of developers and functional staff distributed across the university. Several years ago, Cornell experienced a data breach when an employee’s laptop was stolen.  Centrally stored backup information indicated there was sensitive data on the laptop. With no way of knowing what the criminal intended, the university had to spend significant resources reviewing data, setting up service centers to handle constituent concerns, and provide free credit checks and identity theft protection services—all of which cost money and took time away from other projects. To avoid this issue in the future Cornell came up with several options; one of which was to sanitize the testing and training environments. “The project management team was brought in and they developed a project plan and implementation schedule; part of which was to evaluate competing products in the market-space and figure out which one would work best for us.  In the end we chose Oracle’s solution based on its architecture and its functionality.” – Tony Damiani, Database Administration and Business Intelligence, Cornell University The key goals of the project were to mask the elements that were identifiable as sensitive in a consistent and efficient manner, but still support all the previous activities in the non-production environments. Tony concludes,  “What we saw was a very minimal impact on performance. The masking process added an additional three hours to our refresh window, but it was well worth that time to secure the environment and remove the sensitive data. I think some other key points you can keep in mind here is that there was zero impact on the production environment. Oracle Data Masking works in non-production environments only. Additionally, the risk of exposure has been significantly reduced and the impact to business was minimal.” With Oracle Data Masking organizations like Cornell can: Make application data securely available in non-production environments Prevent application developers and testers from seeing production data Use an extensible template library and policies for data masking automation Gain the benefits of referential integrity so that applications continue to work Listen to the podcast to hear the complete interview.  Learn more about Oracle Data Masking by registering to watch this SANS Institute Webcast and view this short demo.

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  • Removing Barriers to Create Effective Data Models

    After years of creating and maintaining data models, I have started to notice common barriers that decrease the accuracy and usefulness of models. In my opinion, the main causes of these barriers are the lack of knowledge and communication from within a company. The lack of knowledge in regards to data models or data modeling can take many forms. Company Culture Knowledge Whether documented or undocumented, existing business rules of a company can affect how data is modeled. For example, if a company only allows 1 assigned person per customer to be able to manipulate a customer’s record then then a data model that includes an associated table that joins customers and employee’s would be unneeded because that would allow for the possibility of multiple employees to handle a customer because of the potential for a many to many relationship between Customers and Employees. Technical Knowledge Depending on the data modeler’s proficiency in modeling data they can inadvertently cause issues and/or complications with a design without even noticing. It is important that companies share data modeling responsibilities so that the models are developed from multiple perspectives of a system, company and the original problem.  In addition, the tools that a company selects to create data models can also affect the accuracy of the model if designer are not familiar with the tools or the tools are too complex to use for the designer. Existing System Knowledge In order for a data modeler to model data for an existing system so that new changes can be applied to a system then they need to at least know the basic concepts of a system so that they can work within it. This will promote reusability of data and prevent the chance of duplicating data. Project Knowledge This should be pretty obvious, but it is very hard to create an accurate data model without knowing what data needs to be modeled. I have always found it strange that I have been asked to start modeling data prior to a client formalizing any requirements. Usually when this happens I have to make several iterations to a model, and the client still does not know exactly what they want.  In addition additional issues can arise when certain stakeholders of a project are not consulted prior to the design or after the project is over because it can cause miss understandings and confusion by the end user as well as possibly not solving the original problem for which a project is intended to solve. One common thread between each type of knowledge is that they can all be avoided through the use of good communication. For example, if a modeler is new to a company then they should ask older employees about any business specific rules that may be documented or undocumented that must be applied to projects in general. Furthermore, if a modeler is not really familiar with a specific data modeling software then they need to speak up and ask for help form other employees or their manager. This will not only help the modeler in the project, but also help them in future projects that they do for the company. Additionally, if a project is not clearly defined prior to a data modeler being assigned the modeling project then it is their responsibility to communicate with the other stakeholders to clarify any part of a project that is unclear so that the data model that is created is accurately aligned with a project.

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  • How often do you use data structures (ie Binary Trees, Linked Lists) in your jobs/side projects?

    - by Chris2021
    It seems to me that, for everyday use, more primitive data structures like arrays get the job done just as well as a binary tree would. My question is how common is to use these structures when writing code for projects at work or projects that you pursue in your free time? I understand the better insertion time/deletion time/sorting time for certain structures but would that really matter that much if you were working with a relatively small amount of data?

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  • Why would you use data structures (ie Binary Trees, Linked Lists) in your jobs/side projects? [closed]

    - by Chris2021
    It seems to me that, for everyday use, more primitive data structures like arrays get the job done just as well as a binary tree would. My question is how common is to use these structures when writing code for projects at work or projects that you pursue in your free time? I understand the better insertion time/deletion time/sorting time for certain structures but would that really matter that much if you were working with a relatively small amount of data?

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  • Queued Loadtest to remove Concurrency issues using Shared Data Service in OpenScript

    - by stefan.thieme(at)oracle.com
    Queued Processing to remove Concurrency issues in Loadtest ScriptsSome scripts act on information returned by the server, e.g. act on first item in the returned list of pending tasks/actions. This may lead to concurrency issues if the virtual users simulated in a load test scenario are not synchronized in some way.As the load test cases should be carried out in a comparable and straight forward manner simply cancel a transaction in case a collision occurs is clearly not an option. In case you increase the number of virtual users this approach would lead to a high number of requests for the early steps in your transaction (e.g. login, retrieve list of action points, assign an action point to the virtual user) but later steps would be rarely visited successfully or at all, depending on the application logic.A way to tackle this problem is to enqueue the virtual users in a Shared Data Service queue. Only the first virtual user in this queue will be allowed to carry out the critical steps (retrieve list of action points, assign an action point to the virtual user) in your transaction at any one time.Once a virtual user has passed the critical path it will dequeue himself from the head of the queue and continue with his actions. This does theoretically allow virtual users to run in parallel all steps of the transaction which are not part of the critical path.In practice it has been seen this is rarely the case, though it does not allow adding more than N users to perform a transaction without causing delays due to virtual users waiting in the queue. N being the time of the total transaction divided by the sum of the time of all critical steps in this transaction.While this problem can be circumvented by allowing multiple queues to act on individual segments of the list of actions, e.g. per country filter, ends with 0..9 filter, etc.This would require additional handling of these additional queues of slots for the virtual users at the head of the queue in order to maintain the mutually exclusive access to the first element in the list returned by the server at any one time of the load test. Such an improved handling of multiple queues and/or multiple slots is above the subject of this paper.Shared Data Services Pre-RequisitesStart WebLogic Server to host Shared Data ServicesYou will have to make sure that your WebLogic server is installed and started. Shared Data Services may not work if you installed only the minimal installation package for OpenScript. If however you installed the default package including OLT and OTM, you may follow the instructions below to start and verify WebLogic installation.To start the WebLogic Server deployed underneath of Oracle Load Testing and/or Oracle Test Manager you can go to your Start menu, Oracle Application Testing Suite and select the Restart Oracle Application Testing Suite Application Service entry from the Tools submenu.To verify the service has been started you can run the Microsoft Management Console for Services by Selecting Run from the Start Menu and entering services.msc. Look for the entry that reads Oracle Application Testing Suite Application Service, once it has changed it status from Starting to Started you can proceed to verify the login. Please note that this may take several minutes, I would say up to 10 minutes depending on the strength of your CPU horse-power.Verify WebLogic Server user credentialsYou will have to make sure that your WebLogic Server is installed and started. Next open the Oracle WebLogic Server Adminstration Console on http://localhost:8088/console.It may take a while until the application is deployed and started. It may display the following until the Administration Console has been deployed on the fly.Afterwards you can login using the username oats and the password that you selected during install time for your Application Testing Suite administrative purposes.This will bring up the Home page of you WebLogic Server. You have actually verified that you are able to login with these credentials already. However if you want to check the details, navigate to Security Realms, myrealm, Users and Groups tab.Here you could add users to your WebLogic Server which could be used in the later steps. Details on the Groups required for such a custom user to work are exceeding this quick overview and have to be selected with the WebLogic Server Adminstration Guide in mind.Shared Data Services pre-requisites for Load testingOpenScript Preferences have to be set to enable Encryption and provide a default Shared Data Service Connection for Playback.These are pre-requisites you want to use for load testing with Shared Data Services.Please note that the usage of the Connection Parameters (individual directive in the script) for Shared Data Services did not playback reliably in the current version 9.20.0370 of Oracle Load Testing (OLT) and encryption of credentials still seemed to be mandatory as well.General Encryption settingsSelect OpenScript Preferences from the View menu and navigate to the General, Encryption entry in the tree on the left. Select the Encrypt script data option from the list and enter the same password that you used for securing your WebLogic Server Administration Console.Enable global shared data access credentialsSelect OpenScript Preferences from the View menu and navigate to the Playback, Shared Data entry in the tree on the left. Enable the global shared data access credentials and enter the Address, User name and Password determined for your WebLogic Server to host Shared Data Services.Please note, that you may want to replace the localhost in Address with the hosts realname in case you plan to run load tests with Loadtest Agents running on remote systems.Queued Processing of TransactionsEnable Shared Data Services Module in Script PropertiesThe Shared Data Services Module has to be enabled for each Script that wants to employ the Shared Data Service Queue functionality in OpenScript. It can be enabled under the Script menu selecting Script Properties. On the Script Properties Dialog select the Modules section and check Shared Data to enable Shared Data Service Module for your script. Checking the Shared Data Services option will effectively add a line to your script code that adds the sharedData ScriptService to your script class of IteratingVUserScript.@ScriptService oracle.oats.scripting.modules.sharedData.api.SharedDataService sharedData;Record your scriptRecord your script as usual and then add the following things for Queue handling in the Initialize code block, before the first step and after the last step of your critical path and in the Finalize code block.The java code to be added at individual locations is explained in the following sections in full detail.Create a Shared Data Queue in InitializeTo create a Shared Data Queue go to the Java view of your script and enter the following statements to the initialize() code block.info("Create queueA with life time of 120 minutes");sharedData.createQueue("queueA", 120);This will create an instantiation of the Shared Data Queue object named queueA which is maintained for upto 120 minutes.If you want to use the code for multiple scripts, make sure to use a different queue name for each one here and in the subsequent steps. You may even consider to use a dynamic queueName based on filters of your result list being concurrently accessed.Prepare a unique id for each IterationIn order to keep track of individual virtual users in our queue we need to create a unique identifier from the virtual user id and the used username right after retrieving the next record from our databank file.getDatabank("Usernames").getNextDatabankRecord();getVariables().set("usernameValue1","VU_{{@vuid}}_{{@iterationnum}}_{{db.Usernames.Username}}_{{@timestamp}}_{{@random(10000)}}");String usernameValue = getVariables().get("usernameValue1");info("Now running virtual user " + usernameValue);As you can see from the above code block, we have set the OpenScript variable usernameValue1 to VU_{{@vuid}}_{{@iterationnum}}_{{db.Usernames.Username}}_{{@timestamp}}_{{@random(10000)}} which is a concatenation of the virtual user id and the iterationnumber for general uniqueness; as well as the username from our databank, the timestamp and a random number for making it further unique and ease spotting of errors.Not all of these fields are actually required to make it really unique, but adding the queue name may also be considered to help troubleshoot multiple queues.The value is then retrieved with the getVariables.get() method call and assigned to the usernameValue String used throughout the script.Please note that moving the getDatabank("Usernames").getNextDatabankRecord(); call to the initialize block was later considered to remove concurrency of multiple virtual users running with the same userid and therefor accessing the same "My Inbox" in step 6. This will effectively give each virtual user a userid from the databank file. Make sure you have enough userids to remove this second hurdle.Enqueue and attend Queue before Critical PathTo maintain the right order of virtual users being allowed into the critical path of the transaction the following pseudo step has to be added in front of the first critical step. In the case of this example this is right in front of the step where we retrieve the list of actions from which we select the first to be assigned to us.beginStep("[0] Waiting in the Queue", 0);{info("Enqueued virtual user " + usernameValue + " at the end of queueA");sharedData.offerLast("queueA", usernameValue);info("Wait until the user is the first in queueA");String queueValue1 = null;do {// we wait for at least 0.7 seconds before we check the head of the// queue. This is the time it takes one user to move through the// critical path, i.e. pass steps [5] Enter country and [6] Assign// to meThread.sleep(700);queueValue1 = (String) sharedData.peekFirst("queueA");info("The first user in queueA is currently: '" + queueValue1 + "' " + queueValue1.getClass() + " length " + queueValue1.length() );info("The current user is '"+ usernameValue + "' " + usernameValue.getClass() + " length " + usernameValue.length() + ": indexOf " + usernameValue.indexOf(queueValue1) + " equals " + usernameValue.equals(queueValue1) );} while ( queueValue1.indexOf(usernameValue) < 0 );info("Now the user is the first in queueA");}endStep();This will enqueue the username to the tail of our Queue. It will will wait for at least 700 milliseconds, the time it takes for one user to exit the critical path and then compare the head of our queue with it's username. This last step will be repeated while the two are not equal (indexOf less than zero). If they are equal the indexOf will yield a value of zero or larger and we will perform the critical steps.Dequeue after Critical PathAfter the virtual user has left the critical path and complete its last step the following code block needs to dequeue the virtual user. In the case of our example this is right after the action has been actually assigned to the virtual user. This will allow the next virtual user to retrieve the list of actions still available and in turn let him make his selection/assignment.info("Get and remove the current user from the head of queueA");String pollValue1 = (String) sharedData.pollFirst("queueA");The current user is removed from the head of the queue. The next one will now be able to match his username against the head of the queue.Clear and Destroy Queue for FinishWhen the script has completed, it should clear and destroy the queue. This code block can be put in the finish block of your script and/or in a separate script in order to clear and remove the queue in case you have spotted an error or want to reset the queue for some reason.info("Clear queueA");sharedData.clearQueue("queueA");info("Destroy queueA");sharedData.destroyQueue("queueA");The users waiting in queueA are cleared and the queue is destroyed. If you have scripts still executing they will be caught in a loop.I found it better to maintain a separate Reset Queue script which contained only the following code in the initialize() block. I use to call this script to make sure the queue is cleared in between multiple Loadtest runs. This script could also even be added as the first in a larger scenario, which would execute it only once at very start of the Loadtest and make sure the queues do not contain any stale entries.info("Create queueA with life time of 120 minutes");sharedData.createQueue("queueA", 120);info("Clear queueA");sharedData.clearQueue("queueA");This will create a Shared Data Queue instance of queueA and clear all entries from this queue.Monitoring QueueWhile creating the scripts it was useful to monitor the contents, i.e. the current first user in the Queue. The following code block will make sure the Shared Data Queue is accessible in the initialize() block.info("Create queueA with life time of 120 minutes");sharedData.createQueue("queueA", 120);In the run() block the following code will continuously monitor the first element of the Queue and write an informational message with the current username Value to the Result window.info("Monitor the first users in queueA");String queueValue1 = null;do {queueValue1 = (String) sharedData.peekFirst("queueA");if (queueValue1 != null)info("The first user in queueA is currently: '" + queueValue1 + "' " + queueValue1.getClass() + " length " + queueValue1.length() );} while ( true );This script can be run from OpenScript parallel to a loadtest performed by the Oracle Load Test.However it is not recommend to run this in a production loadtest as the performance impact is unknown. Accessing the Queue's head with the peekFirst() method has been reported with about 2 seconds response time by both OpenScript and OTL. It is advised to log a Service Request to see if this could be lowered in future releases of Application Testing Suite, as the pollFirst() and even offerLast() writing to the tail of the Queue usually returned after an average 0.1 seconds.Debugging QueueWhile debugging the scripts the following was useful to remove single entries from its head, i.e. the current first user in the Queue. The following code block will make sure the Shared Data Queue is accessible in the initialize() block.info("Create queueA with life time of 120 minutes");sharedData.createQueue("queueA", 120);In the run() block the following code will remove the first element of the Queue and write an informational message with the current username Value to the Result window.info("Get and remove the current user from the head of queueA");String pollValue1 = (String) sharedData.pollFirst("queueA");info("The first user in queueA was currently: '" + pollValue1 + "' " + pollValue1.getClass() + " length " + pollValue1.length() );ReferencesOracle Functional Testing OpenScript User's Guide Version 9.20 [E15488-05]Chapter 17 Using the Shared Data Modulehttp://download.oracle.com/otn/nt/apptesting/oats-docs-9.21.0030.zipOracle Fusion Middleware Oracle WebLogic Server Administration Console Online Help 11g Release 1 (10.3.4) [E13952-04]Administration Console Online Help - Manage users and groupshttp://download.oracle.com/docs/cd/E17904_01/apirefs.1111/e13952/taskhelp/security/ManageUsersAndGroups.htm

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  • New version of SQL Server Data Tools is now available

    - by jamiet
    If you don’t follow the SQL Server Data Tools (SSDT) blog then you may not know that two days ago an updated version of SSDT was released (and by SSDT I mean the database projects, not the SSIS/SSRS/SSAS stuff) along with a new version of the SSDT Power Tools. This release incorporates a an updated version of the SQL Server Data Tier Application Framework (aka DAC Framework, aka DacFX) which you can read about on Adam Mahood’s blog post SQL Server Data-Tier Application Framework (September 2012) Available. DacFX is essentially all the gubbins that you need to extract and publish .dacpacs and according to Adam’s post it incorporates a new feature that I think is very interesting indeed: Extract DACPAC with data – Creates a database snapshot file (.dacpac) from a live SQL Server or Windows Azure SQL Database that contains data from user tables in addition to the database schema. These packages can be published to a new or existing SQL Server or Windows Azure SQL Database using the SqlPackage.exe Publish action. Data contained in package replaces the existing data in the target database. In short, .dacpacs can now include data as well as schema. I’m very excited about this because one of my long-standing complaints about SSDT (and its many forebears) is that whilst it has great support for declarative development of schema it does not provide anything similar for data – if you want to deploy data from your SSDT projects then you have to write Post-Deployment MERGE scripts. This new feature for .dacpacs does not change that situation yet however it is a very important pre-requisite so I am hoping that a feature to provide declaration of data (in addition to declaration of schema which we have today) is going to light up in SSDT in the not too distant future. Read more about the latest SSDT, Power Tools & DacFX releases at: Now available: SQL Server Data Tools - September 2012 update! by Janet Yeilding New SSDT Power Tools! Now for both Visual Studio 2010 and Visual Studio 2012 by Sarah McDevitt SQL Server Data-Tier Application Framework (September 2012) Available by Adam Mahood @Jamiet

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  • How Oracle Data Integration Customers Differentiate Their Business in Competitive Markets

    - by Irem Radzik
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 With data being a central force in driving innovation and competing effectively, data integration has become a key IT approach to remove silos and ensure working with consistent and trusted data. Especially with the release of 12c version, Oracle Data Integrator and Oracle GoldenGate offer easy-to-use and high-performance solutions that help companies with their critical data initiatives, including big data analytics, moving to cloud architectures, modernizing and connecting transactional systems and more. In a recent press release we announced the great momentum and analyst recognition Oracle Data Integration products have achieved in the data integration and replication market. In this press release we described some of the key new features of Oracle Data Integrator 12c and Oracle GoldenGate 12c. In addition, a few from our 4500+ customers explained how Oracle’s data integration platform helped them achieve their business goals. In this blog post I would like to go over what these customers shared about their experience. Land O’Lakes is one of America’s premier member-owned cooperatives, and offers an extensive line of agricultural supplies, as well as production and business services. Rich Bellefeuille, manager, ETL & data warehouse for Land O’Lakes told us how GoldenGate helped them modernize their critical ERP system without impacting service and how they are moving to new projects with Oracle Data Integrator 12c: “With Oracle GoldenGate 11g, we've been able to migrate our enterprise-wide implementation of Oracle’s JD Edwards EnterpriseOne, ERP system, to a new database and application server platform with minimal downtime to our business. Using Oracle GoldenGate 11g we reduced database migration time from nearly 30 hours to less than 30 minutes. Given our quick success, we are considering expansion of our Oracle GoldenGate 12c footprint. We are also in the midst of deploying a solution leveraging Oracle Data Integrator 12c to manage our pricing data to handle orders more effectively and provide a better relationship with our clients. We feel we are gaining higher productivity and flexibility with Oracle's data integration products." ICON, a global provider of outsourced development services to the pharmaceutical, biotechnology and medical device industries, highlighted the competitive advantage that a solid data integration foundation brings. Diarmaid O’Reilly, enterprise data warehouse manager, ICON plc said “Oracle Data Integrator enables us to align clinical trials intelligence with the information needs of our sponsors. It helps differentiate ICON’s services in an increasingly competitive drug-development industry."  You can find more info on ICON's implementation here. A popular use case for Oracle GoldenGate’s real-time data integration is offloading operational reporting from critical transaction processing systems. SolarWorld, one of the world’s largest solar-technology producers and the largest U.S. solar panel manufacturer, implemented Oracle GoldenGate for real-time data integration of manufacturing data for fast analysis. Russ Toyama, U.S. senior database administrator for SolarWorld told us real-time data helps their operations and GoldenGate’s solution supports high performance of their manufacturing systems: “We use Oracle GoldenGate for real-time data integration into our decision support system, which performs real-time analysis for manufacturing operations to continuously improve product quality, yield and efficiency. With reliable and low-impact data movement capabilities, Oracle GoldenGate also helps ensure that our critical manufacturing systems are stable and operate with high performance."  You can watch the full interview with SolarWorld's Russ Toyama here. Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Starwood Hotels and Resorts is one of the many customers that found out how well Oracle Data Integration products work with Oracle Exadata. Gordon Light, senior director of information technology for StarWood Hotels, says they had notable performance gain in loading Oracle Exadata reporting environment: “We leverage Oracle GoldenGate to replicate data from our central reservations systems and other OLTP databases – significantly decreasing the overall ETL duration. Moving forward, we plan to use Oracle GoldenGate to help the company achieve near-real-time reporting.”You can listen about Starwood Hotels' implementation here. Many companies combine the power of Oracle GoldenGate with Oracle Data Integrator to have a single, integrated data integration platform for variety of use cases across the enterprise. Ufone is another good example of that. The leading mobile communications service provider of Pakistan has improved customer service using timely customer data in its data warehouse. Atif Aslam, head of management information systems for Ufone says: “Oracle Data Integrator and Oracle GoldenGate help us integrate information from various systems and provide up-to-date and real-time CRM data updates hourly, rather than daily. The applications have simplified data warehouse operations and allowed business users to make faster and better informed decisions to protect revenue in the fast-moving Pakistani telecommunications market.” You can read more about Ufone's use case here. In our Oracle Data Integration 12c launch webcast back in November we also heard from BT’s CTO Surren Parthab about their use of GoldenGate for moving to private cloud architecture. Surren also shared his perspectives on Oracle Data Integrator 12c and Oracle GoldenGate 12c releases. You can watch the video here. These are only a few examples of leading companies that have made data integration and real-time data access a key part of their data governance and IT modernization initiatives. They have seen real improvements in how their businesses operate and differentiate in today’s competitive markets. You can read about other customer examples in our Ebook: The Path to the Future and access resources including white papers, data sheets, podcasts and more via our Oracle Data Integration resource kit. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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  • Tellago is still hiring….

    - by gsusx
    Tellago 's SOA practice is rapidly growing and we are still hiring. In that sense, we are looking to for Connected Systems (WCF, BizTalk, WF) experts who are passionate about building game changing solutions with the latest Microsoft technologies. You will be working alongside technology gurus like DonXml , Pablo Cibraro or Dwight Goins . If you are interested and not afraid of working with a bunch of crazy people ;)please drop me a line at jesus dot rodriguez at tellago dot com. Hope to hear from...(read more)

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