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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How to Compare CSV Files or Compare a CSV File to a Database Table


CSV files are a quick and convenient way to record structured data in a generic format. Because CSV files are so easy to create, multiple similar versions of very large CSV files can quickly proliferate. Often it becomes necessary to compare CSV files to find the desired version. In an ETL (Extract Transform Load) scenario, a data analyst may want to compare a CSV file to a database table for validation or to update data.

DiffDog, the unique XML-aware diff / merge tool from Altova, supports CSV as a native file format for comparison and can compare and selectively merge data CSV to CSV, or between a CSV file and database table. Let’s look at an example.

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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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Database Mapping with Database Exception Handling


Critical business processes depend on reliable data and database administrators and other data analysts want to be confident in the integrity of information stored in database tables. During automated ETL (Extract Transform Load) operations or other database import tasks, invalid data might be encountered that jeopardizes success of the procedure. Altova MapForce includes database exception handling to roll back the affected data when an error occurs and optionally proceed with the rest of a database mapping.

For instance, an error in a single record need not prevent execution of a mapping from continuing, such as when certain database constraints prevent the mapping from inserting or updating invalid data.

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