Announcing RaptorXML, the Hyper-fast XML and XBRL Server


  clip_image001We are very excited to be at the XBRL 26 Conference in Dublin, Ireland today to announce a brand new server product in the Altova family of XML and XBRL tools! Altova RaptorXML is a hyper-fast XML and XBRL validation and processing server. It’s Altova’s third-generation XML and XBRL engine, built completely from scratch to help organizations efficiently validate, process, transform, and query the vast and ever-increasing amounts XML and XBRL data being generated as a result of XBRL compliance regulations and myriad other big data trends. RaptorXML is written to be highly scalable for today’s multi-CPU and multi- core computers and servers. This, along with high performance code optimizations and an extremely low memory footprint, has helped make RaptorXML a lightning-fast XML and XBRL server that can meet the demands of today’s data processing applications. Simply put: we architected RaptorXML to combine the performance benefits afforded by modern parallel computing environments with strict compliance to the latest versions of all relevant XML and XBRL standards. RaptorXML includes support for the very latest versions of all relevant standards and has been submitted to rigorous regression and conformance testing. The server will be available in two versions, both of which are available for Windows, Linux, and MacOS platforms. RaptorXML Server supports validation and processing of:

  • XML 1.0 & 1.1
  • XInclude 1.0
  • Xlink 1.0
  • XML Schema 1.0 & 1.1
  • XPath 1.0, 2.0 & 3.0
  • XSLT 1.0, 2.0 & 3.0 (subset)
  • XQuery 1.0 & 3.0
  • And more

RaptorXML+XBRL Server supports all the features of RaptorXML Server, with the addition of processing and validating the XBRL family of standards:

  • XBRL 2.1
  • XBRL Dimensions
  • XBRL Formula 1.0
  • XBRL Functions
  • XBRL Definition Links

Developers creating solutions using Altova MissionKit XML development and XBRL development tools will be able to power their server applications with RaptorXML for hyper-performance, increased throughput, and efficient memory utilization, giving them the opportunity to validate and process large amounts of XML or XBRL data cost-effectively.  Check out the complete list of supported XML and XBRL standards and more details on this groundbreaking new server product. RaptorXML will be available to download and purchase in May. clip_image003

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Harness the Power of Chained Transformations in MapForce


MapForce 2011 introduced an exciting enhancement we like to call chained transformations. Chained transformations let you create complex mappings where the output of one mapping becomes the input to another. In other words, two or more components can be directly connected to a final target component. MapForce chained transformation example MapForce has long supported intermediate components and generation of intermediate output that is supplied as input further down the line in the mapping. The new enhancement provides a direct route from your original input to your final target output. Pass Through for the Express Route to Data Integration Mapping and debugging a series of intermediate components can prove to be time consuming and cumbersome, especially when you are working with huge data stores. The new Pass Through button lets you efficiently go straight to your final target. The intermediate components of a chained transformation include a Pass Through button and a Preview button, and the final component also includes a Preview button. MapForce Pass-Through and Preview buttons Activating the Pass Through button on the intermediate component disables the Preview button for that component, and the intermediate output is sent directly to the next component for transformation. You don’t have to explicitly specify input and output data file names for the intermediate component in the component’s Properties dialog. Instead, MapForce automatically supplies default file names. The MapForce Output Preview window displays the final target output from the last component in the chain. MapForce chained transformation preview window In case you want to examine the intermediate output as you design and verify your mapping, the arrow buttons at the top left or the drop-down menu at the top right let you preview intermediate data. MapForce chined transformation preview window If our mapping included multiple intermediate components, the Pass Through feature would let us inspect each stage of the transformation in a single output window. Integrating All the Local Components In other data integration projects you may want to save the data from intermediate transformations as well as the output from your final target component. When you deactivate the Pass Through button of the intermediate component, you can select either component for preview. Note that you can specify the name of the output file for the intermediate component in the Properties dialog, or you can let MapForce supply a default name. MapForce Preview button If you select the intermediate component for preview, as shown above, the Output Preview Window displays only the intermediate output. MapForce Output Preview window If the Pass Through button is deactivated and you select the final component for preview, only the final result is displayed in the Output Preview window. MapForce Preview button MapForce Output Preview window Generate Code for Your Mapping If you will need to perform repetitive transformations, MapForce lets you generate royalty-free code for your chained transformation in XSLT 1.0, XSLT 2.0, XQuery, Java, C#, and C++. All this functionality is designed to give today’s developers and data management professionals ultimate flexibility and automation to meet 21st-century data communication requirements. See for yourself how easy it is to build a chained transformation for your own data integration project. Download a free 30-day trial of MapForce!

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Using Altova Tools to Work with XML Data in the SQL Azure Cloud


In an earlier post we discussed connecting to Microsoft SQL Azure databases with Altova DatabaseSpy and demonstrated database schema comparison and content comparison between a local database and the same database migrated to SQL Azure. In this post we will use a different method to migrate an existing table to SQL Azure and show you some tricks you can do with XML in the cloud. We started by creating a new database schema in SQL Azure. Then we created a DatabaseSpy project with a connection to a local copy of SQL Server Express running the AdventureWorks sample database, and a second connection to our new SQL Azure schema. DatabaseSpy Project helper window The AdventureWorks database contains a table called JobCandidate with some XML data we will use for a model for our SQL Azure XML contents. AdventureWorks JobCandidate table in the DatabaseSpy Online Browser window We can generate a CREATE statement for the existing table to use as a basis for the SQL Azure version. CREATE statement for the existing JobCandidate table We need to modify this statement to execute in our SQL Azure database. In addition to changing the database and schema names, we will remove the foreign key constraint to the Employee table, since our new database doesn’t contain a table with that name. Also, SQL Azure does not support the CONTENT keyword, so we will remove that as well. Modifying the CREATE statement in a DatabaseSpy SQL Editor window After making sure the Properties window for the revised CREATE statement points to the SQL Azure database, we can execute the statement. When we refresh the database and expand our view in the Online Browser helper window, we can see the new empty table. New table viewed in the DatabaseSpy Online Browser window A data comparison between the existing table and the new one will allow us to create a script to migrate data into our new table in the SQL Azure cloud. This is similar to the data comparison we wrote about in our previous post on SQL Azure, except instead of merging data directly, we will save the merge script. DatabaseSpy Merge Data dialog Our first attempt to run the merger script failed, throwing an error message that SQL Azure cannot insert values into the new table when IDENTITY_INSERT is set to OFF. We can add a line to the merge script to SET INDENTITY_INSERT ON and re-execute: Merge data script viewed in a DatabaseSpy SQL Editor window Next, we can run a SELECT query to view the data that was successfully uploaded. Select query and Results viewed in DatabaseSpy The DatabaseSpy Data Inspector window lets us more easily examine the contents of a wide column, and is ideal to use for XML documents stored in the Resume column of the new JobCandidate table. DatabaseSpy Data Inspector window Editing XML Data with XMLSpy If you need to revise, edit, update, or validate XML data in a SQL Azure database, Altova XMLSpy provides more robust XML editing features than DatabaseSpy. We can connect to our SQL Azure database from XMLSpy and run a SELECT query from the XMLSpy Database Query window. XMLSpy lets us open any XML row for direct editing, with access to advanced XML editing functionality. XMLSpy Database Query view of XML data in the SQL Azure cloud Of course all the familiar features of the XMLSpy text view and grid view are available. XMLSpy Text view of XML data in the SQL Azure cloud XMLSpy Grid view of XML data inthe SQL Azure cloud After your edits are complete, the XMLSpy File / Save menu option saves the revised XML document to the same row of the JobCandidate table in the SQL Azure database in the cloud. XMLSpy File / Save menu option saves XML data to the SQL Azure cloud Parsing XML Data with XQuery You can also apply the XMLSpy XQuery editor, with its built-in knowledge of XQuery syntax and context-sensitive entry helpers to build XQuery statements that parse the XML data in your SQL Azure database. The XQuery statement below extracts and returns the home addresses from the XML resumes where JobCandidateID is less than 7. XMLSpy XQuery editor for XML data in SQL Azure The XQuery statement can be executed in the Database Query window, with results immediately available to work with in XMLSpy. XQuery statement and results inthe XMLSpy Database Query window Of course the XQuery result can also be edited in Text view or in Grid view. XQuery result in XMLSpy Text view XQuery result in XMLSpy Grid view And you can save the query result either from the Database Query window or from the XML Editor view. Find out for yourself how productive you can be by using Altova tools to work with XML data in the SQL Azure cloud ̶ download a free 30-day trial of the Altova MissionKit for Software Architects, an integrated suite that includes XMLSpy, DatabaseSpy, and additional XML, database, and UML tools.

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Working with XML in Databases


More and more users are storing XML documents in database columns, especially when XML data is sent or received from other entities. Storing data in XML helps enterprises more easily accommodate revisions to industry-standard data formats as XML Schemas evolve over time. One challenge in migrating from a relational database to an XML-oriented database application is developing queries that replace traditional SQL queries of relational data to parse XML documents stored in the database. We recently had an opportunity to address XQuery for XML in databases in a presentation titled Altova Tools for DB2® in a teleconference sponsored by IBM® for the pureXML™ Devotees user group. After an introduction and brief background on Altova, we focused on the special functionality included in XMLSpy to manage XML Schemas in DB2 and to edit XML data stored in DB2. The XMLSpy Database Query Window makes it easy to edit XML database content directly in XMLSpy. XMLSpy_editDB2_blog Altova has built specialized capabilities for deep integration of Altova tools with the DB2 pureXML data server to help customers working with XML, XML Schema, XQuery, and other XML-related technologies. We demonstrated the XMLSpy XQuery editor, XQuery debugger and XQuery profiler, with support for executing XQuery scripts directly against the DB2 database and for the special DB2 xmlcolumn and sqlquery operators. We closed the presentation with a walk-through of the steps a user can take to migrate legacy relational data to an XML-based application, including inferring an XML Schema from relational data in a table in DB2, then importing data from the table and automatically tagging it in XML according to the new XML Schema. We have uploaded a PDF file the slides from the presentation on SlideShare. You can also get a copy at the IBM pureXML Devotees page, where you can listen to the recorded audio as well. The best way to experience for yourself how well the features of XMLSpy, MapForce, StyleVision, and DatabaseSpy work with DB2 and other databases with XML is to click here to download a free trial of the Altova MissionKit.

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