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Support for JSON5 in Altova MissionKit, Server Products, and MobileTogether


Altova products have supported JSON for several years. Now, Version 2017 Release 3 of MissionKit and Server products, and MobileTogether Version 3.2 all include support for JSON5 across the product line.

The JSON data format was originally designed to be machine-written and consumed, promoting efficient communication between servers. Usage has expanded and JSON5 is a proposed extension intended to make JSON code easier for humans to write and read.  JSON5 extends JSON by adding some ECMAScript 5 features and, like JSON, is a strict subset of JavaScript. Specifically, JSON5 permits inline and block comments, allows long strings to be split over several lines, and defines alternate legal syntax options for quotes and commas.  These features are not permitted in standard JSON, so files containing the proposed enhancements are typically identified with the .json5 filename suffix.

This post details specific support for JSON5 in each Altova product.

Learn about JSON5 support in Altova tools

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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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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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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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MapForce Supports SQL Merge When It’s the Right Tool for the Job


Large database tables can easily contain a million, even hundreds of millions of rows of data. Database administrators and others charged with maintaining such large datasets are always concerned about execution time for ETL (Extract, Transform, and Load) operations, updates, and other SQL queries. To make these operations more efficient, some — but not all — database vendors implemented a SQL merge statement to insert or update rows of an existing table as a single bulk-insert statement rather than requiring individual statements for each row.

Altova MapForce automatically supports SQL merge when it is available for the target database. Let’s look at an example.

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