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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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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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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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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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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