Posts

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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CbC Reporting Made Easy


A recent mandate from the OECD called on large, multinational companies to report financials annually for each country in which they do business to their local tax authority. The OECD specified that this detailed Country by Country (CbC) Report be filed in an XML document according to their reporting schema. But for tax departments that work largely in Excel or other accounting software, this presented a significant stumbling block – and companies found themselves scrambling to meet the requirements by the deadline.

What was needed is a way to automatically generate valid, properly formatted CbC XML reports based on existing data. Altova created the Country by Country Reporting Solution to do just that, either based on manually entered data or figures imported directly from Excel. Let’s take a look at how it works.

 

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Transitioning Data Mapping Projects from Development through Testing and Production


Data mapping projects often mirror software development efforts with distinct phases for design, testing, and deployment. This is especially true for ETL (Extract Transform Load) projects when repeated data mapping execution is required as new data becomes available, and the stakes increase higher with large data sets. The Altova MissionKit and Server Software products provide Global Resources to define configurations for each project phase and smoothly transition between them.

Let’s take a look at an example based on a MapForce data mapping from a source file to a database.

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Handle HTTP Errors During Automated Data Integration


Data analysts and other professionals often need to generate real-time data through automated execution of data mappings that request Web services and save the results. During automated execution it’s important to gracefully handle any unexpected HTTP error rather than terminate the integration task.

In an earlier post we discussed conditional processing of a REST Web service response to handle HTTP errors, where separate output files were generated for a normal response and an error. Now let’s look at a revised mapping solution for the airport status example to generate a single mapping result file that contains either the requested airport status or a description of the error.

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Handling HTTP Errors in Web Service Data Mappings


Data integration projects that include information from external Web services may be vulnerable to HTTP errors when retrieving remote data. When data mappings run under automated control it’s especially important to detect and report errors even if errors only occur very rarely.

A MapForce data mapping can include Web service calls and output the result directly to a file or database, or combine it with other inputs for further processing. Regardless of the final output, an HTTP Web service error encountered in a REST Web service request puts the mapping at risk.

MapForce includes features for handling HTTP errors instead of simply aborting execution of a mapping. Developers can configure the body of a REST Web service call to handle and report exceptions based on the HTTP status code returned.

Let’s look at an example.

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