Data Mapping with XSLT3 Math Functions


XSLT3 adds trigonometry and other advanced math functions, new formatting functions, functions to collect environment variables, and more, extending XSLT and XSLT2 XML transformation standards. Data analysts and other data professionals can apply XSLT3 functions to solve XML data mapping and integration challenges that require complex mathematical computations. Let’s look at some MapForce examples of data mapping with XSLT3 math functions using trigonometry and other complex math expressions.

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Data Mapping Binary Objects – Part 2


Binary objects – BLOBs — can be cumbersome to manage in databases. In an earlier post we described a MapForce data mapping to insert binary objects into a database with generated metadata to identify the BLOBs later. The companion challenge in data mapping binary objects is to extract binary data and save it in a comprehensible form faithful to the original.

Let’s look at how that’s done.

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Data Mapping Binary Objects


Binary objects are difficult to manage in databases. They are large, their content is not human readable, and they can contain bytes of data easily misinterpreted as control characters. Even the data type name for binary large objects – BLOB – reflects most database managers’ dislike of them. Before relational databases, the definition of a blob was “something undefined or amorphous.”

Altova MapForce, the award-winning, graphical data mapping tool for any-to-any conversion and integration, includes features for effortlessly data mapping binary objects to or from all popular relational databases. Data such as images, PDF files, video files, or any other binary data can be mapped. Let’s look at an example.

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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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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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