Posts

Use Join to Integrate Data in Any Format


Join is a powerful SQL operation implemented across most database types and familiar to database users. Join is typically used to select and combine information from multiple database tables.

Altova MapForce includes a join component for data mapping that works like a SQL join for database tables and extends data integration functionality by empowering users to join data trees of any data format. Anyone familiar with join operations for database tables will find the MapForce join component especially intuitive. A join operation in MapForce can even combine two different data formats and produce output in a new format altogether.

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How to Debug XSLT and XQuery


Nothing’s more frustrating than getting unintended results from an XSLT or XQuery transformation and having to spend hours tracking down the issue – especially if you’ve inherited the project from another developer or haven’t looked at the code in a few months.  Of course, XMLSpy has long included an XSLT debugger and XQuery debugger for setting break points and stepping through transformations to identify problems. However, the debugging process just got even more interactive and precise with the introduction of XSLT/XQuery back-mapping.

With back-mapping enabled, you can simply click on or hover over the portion of your output document you want to zero in on, and XMLSpy will immediately highlight the source XML and XSLT or XQuery instruction that is responsible. Let’s see how it works.

Debug XSLT with back-mapping

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New XSLT Back-mapping Headlines Altova Release


It’s time for the latest release of Altova desktop developer tools and server software products, and this one introduces numerous innovative features across the product line, including a brand-new version of MapForce Server called MapForce Server Accelerator Edition for even faster processing of data integration jobs.

Let’s take a look at the highlights of Version 2017 Release 3.

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A Typical MapForce Server Use Case


Envision a manufacturing company that controls costs by exploiting a just-in-time assembly process with a very low supply of parts inventory on hand. New customer orders are logged in a sales database and at the end of every day the components needed to assemble that day’s sales are tabulated.

The IT department runs a SQL query to identify the required parts and transforms the list into a purchase order in JSON format to be transmitted to the supply chain.

Sound familiar? Our recent blog series on JSON tools and JSON data mapping were based on this real-life scenario. In this post we describe a MapForce Server use case that automates the repetitive task of generating each day’s purchase order.

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The Only JSON Dev Tool You’ll Need


The popularity of JSON is ever-increasing, and XMLSpy is keeping pace. Over the past few product releases, we’ve added intelligent functionality for editing and converting JSON data to the product. Most recently, we’ve completed the circle with one-click conversion between XML Schemas and JSON Schemas, as well as sample instance generation and JSON Schema documentation generation.

Let’s walk through some common examples demonstrating this functionality – and see how these time-saving tools make XMLSpy the only JSON development tool you’ll need.

JSON Dev Tools

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JSON Data Mapping and Transformation with MapForce


JSON is a popular format for transferring data between systems thanks to its simple markup, small footprint, and heritage based on the JavaScript programming language. MapForce supports JSON as both an input and output format for JSON data mapping and transformation. For instance, MapForce can extract information from any popular database and produce a JSON file ready for transfer.
The Requirement: Here is an example of a typical need for JSON data mapping: A manufacturing company controls costs by exploiting a just-in-time assembly process with very little parts inventory on hand. New customer orders are logged in a sales database, and at the end of every day the components needed to assemble that day’s sales are tabulated via a query into the database. The required parts will be ordered from suppliers via a purchase order transferred in JSON format.

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EDI Data Mapping with MapForce


Any computer industry standard that promotes reliability and clear communication between independent enterprises will have a long life. EDI (Electronic Data Interchange) originated in the 1960s to enable automated transactions between corporate computer systems. EDI remains in widespread use today and continues to evolve to meet modern requirements, under the administration of the UN/EDIFACT and ANSI standards bodies.

Altova MapForce supports EDI data mapping between EDI messages and XML, JSON, relational databases, flat files, Excel, or other data formats to bridge between commonly used information interchange and in-house technologies.

MapForce includes support for the latest EDIFACT versions 2015B and 2016A including the new VERMAS message. Mapping and translating EDIFACT messages to other usable data types for transfer, storage, and management is a common business requirement solved by MapForce.

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