Posts

New tools for JSON, EDI, SWIFT, and XBRL in Version 2021


As standards-based technologies and their applications evolve, so does the functionality that users require from developer tools. In our latest release, we’ve added new ways to work with XBRL, JSON, EDI, and more that help Altova customers work faster and more efficiently.

Let’s take a look at some highlights from the Altova Software Version 2021 release.

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Validating Data Transformation


Software developers and other data professionals often need to transform data from one format to another. These transformations can be simple one-to-one conversions or may require more complex manipulation. For instance, relationships must be generated when importing flat CSV files into a database, or source data may need to be split for the target, as in full name vs. first, middle, last, and optional suffix. Validating data transformation is critical to prevent data loss or corruption.

In our recent post on Web service data integration combined a string value for GMT time with a numeric offset in seconds to generate the local time for weather forecasts. We created a user function that performed all the steps required to complete this operation. MapForce includes a powerful interactive data mapping debugger that can easily trace and validate this transformation. Let’s take a look at how it works.

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Web Service Data Integration


In a previous post we wrote that every data integration and reporting task needs to start with a clear understanding of the source data. Using grid view in XMLSpy, the industry-leading XML and JSON editor, we analyzed JSON data for 5-day weather forecasts retrieved from a Web service.

Continuing with our earlier scenario, we’ll use MapForce, the award-winning, graphical data mapping tool for any-to-any conversion and integration, to map the forecasts for a series of major cargo shipping ports into nicely formatted Excel documents. We’ll want to highlight any predicted high winds or heavy rainfall that could cause delays by interfering with cranes loading and unloading containers, or slowing ships entering and exiting the harbors.

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Excel Data Mapping to Update Existing Documents


Excel began life as a simple spreadsheet tool. Over time, support for rich text styling options, built-in charts, and copy and paste formatting features has led many enterprises to create reports in Excel documents. This can cause difficulty when data changes and existing documents need to be manually updated for distribution to a wide audience in the familiar report style.

Altova MapForce, the award-winning, graphical data mapping tool for any-to-any conversion and integration, supports Excel data mapping to convert data to existing Excel documents while preserving styles and formulas in the original.

This feature lets you write directly to nicely formatted Excel files to update data at runtime: any designated worksheets, rows, and cells from the specified file will be replaced with data from the mapping and all formatting in the existing file will be preserved as-is. To protect functionality in the existing spreadsheet, cells with formulas are not overwritten.

Let’s look at an example:

Financial pros using XBRL
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New Data Integration Tools


Altova MissionKit tools offer numerous ways to connect to, query, and integrate data from disparate sources. With multiple product releases each year, we’re constantly working to deliver increased power and efficiency for data integration, while adding features requested by customers. This includes ongoing updates to built-in support for all major SQL databases across the product line.

Let’s take a look at some of the recently added tools and enhancements.

New data integration tools in Altova's release
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How to Convert Legacy Text Files [Video]


It’s a common requirement to convert non-standard or legacy text files to or from structured data formats like XML, JSON, and relational databases. However, many times legacy text files are not in a format that can be readily processed by data mapping tools, especially when they have a complex and unique structure that does not consistently fit into CSV or fixed-length field patterns. Moreover, sometimes you need to extract only portions of useful data from a legacy text file.

MapForce, Altova’s any-to-any data conversion tool, includes a unique utility called FlexText that makes it easy to visually define templates for parsing text files and making them accessible to the data mapping tool.

See how FlexText works in our video tutorial.

The example files referenced in the video are available here and you can try FlexText with a free, 30-day trial of MapForce.

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Data Mapping JSON Lines


The JSON data format continues to evolve as an open standard as it is creatively applied to new data interchange requirements. JSON Lines, defined at http://jsonlines.org/, is a convenient text format for storing structured data where each record is a single line and a valid JSON object. JSON Lines handles tabular data and clearly identifies data types without ambiguity. This allows records to be processed one at a time, which makes the format very useful for exporting and sending data.

Altova MapForce supports data mapping JSON Lines as either a data source or target. Let’s look at a mapping project to extract records from a database table and map to a JSON Lines file for output.

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