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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Embedding Images in XML


One of the really cool features added to XMLSpy a few years ago based on customer requests is the ability to embed external files – such as images – directly in an XML document as encoded text. This gives you the option to package all required data from various external files together in one large XML document. The functionality is also available for embedding images in JSON documents. 

Let’s take a look at how easy it is to accomplish this in the XML and JSON editor in just a few steps.

Photo for embedding images

 

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Version 2020 Revolutionizes JSON Editing


We’re introducing several exciting new tools for JSON development in Altova Software Version 2020, but there’s much more too. Support for the XULE XBRL standard, comparing CSV against database content, and updated database support are just a few of the new features introduced across the product line.

Let’s take a look at the highlights.

Altova Software v2020 announcement
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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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MapForce 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, 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 any relational 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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Data Mapping Protocol Buffers (Protobuf)


MapForce 2019 supports data mapping protocol buffers with 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 protocol buffers (often abbreviated as protobuf) using Java, Python, C++, C#, Ruby, and other programming languages.

The structure of any protocol buffer 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 other data formats. MapForce supports data mapping protocol buffers using .proto files versions 2 and 3.

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

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Integrating APIs and Mobile Apps


Busy mobile users on the go prefer apps that are convenient and efficient. MobileTogether provides developers with features to seamlessly integrate APIs and mobile apps to combine mobile device functionality with up-to-date information from external sources. This empowers developers to create custom cross-platform native apps that provide a rich and entertaining end-user experience.

Public APIs are a great source of external data to enhance almost any custom mobile app. Developers can combine information from multiple APIs to provide users with better information, faster, in an elegant, integrated package.

APIs are available for almost any kind of information your mobile app may need, from flight tracking to commodity or stock prices to tropical storm tracking.

In this post we’ll look at a GPS app that starts with mobile device geolocation functionality to answer the basic question, “Where am I?” then interfaces with APIs from Google and MapQuest to add a wealth of additional information. We’ll integrate a spatially-aware search engine to locate nearby points of interest as near as a quarter mile radius, all the way to pin-pointing the user’s location in a satellite photograph with a wide-angle view of an entire continent or more.

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