Validating and Debugging Data Transformations

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 an earlier post on Web service data integration, we 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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MapForce Data Mapping Tutorial (Video)

Altova MapForce is an any-to-any data transformation, conversion, and ETL tool for integrating data.

A graphical data mapping tool, MapForce has an intuitive drag-and-drop interface that lets you easily convert data between any two formats, such as XML, JSON, relational databases, NoSQL databases, EDI, and more. It also features an extensive library of conversion functions that can be chained together to form custom functions that can be reused throughout your projects.

Data translated by MapForce can be pulled to or pushed from a relational or NoSQL database and all data management products, and it can be adapted to customize in-house data management solutions.

The MapForce tutorial video below covers all major features offered by the data integration tool and shows example mappings between several different types of files.

You can try MapForce yourself with a free, 30-day trial.

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Transforming and Converting Protobuf

MapForce supports mapping protocol buffers (Protobuf) to and from other structured data formats as mapping sources or targets. In the constant quest for more efficient ways to transfer, manipulate, and manage large structured data sets, Google has created a language- and platform-neutral data format similar to XML, but smaller, faster, and simpler than even JSON data. Tools are available to generate and work with Protobuf using Java, Python, C++, C#, Ruby, and other programming languages.

The structure of any Protobuf message is defined in a .proto file that defines each field name and value type. Altova MapForce lets users drop these .proto files into a data mapping as a source or target along with any other data, including XML, JSON, relational databases, Excel, flat files, REST and SOAP web services, and others.  .proto files versions 2 and 3 are supported.

A MapForce data mapping creates compatibility between existing XML, JSON, database or legacy data formats and new applications leveraging the efficiency of Protobuf.

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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 of how to map Excel data.

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Data Mapping NoSQL Databases

NoSQL databases are non-tabular databases that store data differently than traditional databases made up of relational tables. Two of the most popular NoSQL databases, MongoDB and Apache CouchDB, store data as collections of BSON (binary JSON) and JSON documents. These databases leverage flexible JSON schemas and scale easily with large amounts of data and high user loads.

Altova MapForce has long supported data mapping all popular relational databases and now also includes native support for data mapping NoSQL databases. MapForce includes functionality for inserting, extracting, filtering, and ordering NoSQL data. Let’s look at an example.

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NoSQL Database Support and More in Version 2022

Altova Software Version 2022 is now available, with exciting new support for mapping and converting NoSQL databases in MapForce, pure text report output in StyleVision, and Windows 11 across the product line. The release also adds support for the exciting new OIM standard from XBRL International.

Here’s a look at the highlights.

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