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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Node Functions Simplify Mapping Hierarchical Data Structures


MapForce node functions simplify mapping hierarchical data such as XML nodes or CSV, JSON, EDI, or database fields by permitting users to define a data processing function at the node level and apply it recursively to all descendant items.

Similarly, default values can also be assigned to nodes and automatically applied to descendants.

Defaults and node functions are particularly useful when a data mapping and transformation task requires the same processing logic for multiple descendant items in a structure, for example:

  • Replace null values with some other value, recursively for all descendant items
  • Replace a specific value (for example, “N/A”) with some other value recursively for all descendant items
  • Replace all database null values when reading from a database table
  • Trim all trailing spaces for all values from a source database
  • Append a custom prefix or suffix to all values written to a target file or database
  • Formatting of output values
  • And many others

Defaults and node functions simplify mapping hierarchical data by eliminating need to copy-paste the same function multiple times into a mapping. Repeating the same function unnecessarily clutters the mapping layout and makes it more difficult to understand or revise.

Let’s look at an example.

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Run Altova Server Software on Azure Cloud


The Altova Server Platform is comprised of the complete family of Altova’s high performance server software for automating data processing and data integration workflows. These cross-platform server software products allow for flexible installation either on premises or in any private or public cloud infrastructure.

For customers utilizing the Microsoft Azure cloud, we’ve created a convenient, free VM template with the Altova Server Platform pre-installed for easy deployment, available on the Azure Marketplace.

Altova Server Platform

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CbC Reporting Made Easy


A recent mandate from the OECD calls on large, multi-national companies to report financials annually for each country in which they do business to their local tax authority. The OECD requires that this detailed Country by Country (CbC) Report be filed in XML document according to their reporting schema. But for tax departments that work largely in Excel, this provides a significant stumbling block – and companies are scrambling to meet the requirements by the end of 2017.

The new CbC Reporting Solution from Altova takes the pain out of meeting the mandate by automatically generating valid, properly formatted CbC XML reports based on data either entered manually – or imported directly from Excel. Let’s take a look at how it works.

 

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Data Mapping NCPDP SCRIPT


EDI (Electronic Data Interchange) standards allow participants with different roles in an industry to communicate clearly and rapidly, and date back to the earliest implementations of electronic communication in the 1950s, long before modern business technologies such as ERP, CRM, and many others. Yet even today, EDI standards continue to evolve to support new requirements and opportunities.

MapForce has long supported data mapping to and from ANSI X12, UN/EDIFACT and other popular EDI standards, and now in the latest release adds support for data mapping NCPDP SCRIPT.

SCRIPT is the state of the art EDI standard developed by the National Council for Prescription Drug Programs (NCPDP) for electronically transmitting medical prescriptions, also known as ePrescribing (eRX) in the United States.

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MapForce Server Accelerator Edition Achieves a New Level of Data Transformation Performance


MapForce Server automates recurring execution of data mappings and transformations designed and tested using Altova MapForce. Every day, MapForce Server is employed in business communication, financial reporting, database ETL, and many other applications to transform critical data between any of XML, JSON, database, EDI, XBRL, flat file, CSV, Excel, and/or Web service formats.

Now, MapForce Server Accelerator Edition offers even faster throughput for high-performance server platforms.

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Use Join to Integrate Data in Any Format


Join is a powerful SQL operation implemented across most database types and familiar to database users. Join is typically used to select and combine information from multiple database tables.

Altova MapForce includes a join component for data mapping that works like a SQL join for database tables and extends data integration functionality by empowering users to join data trees of any data format. Anyone familiar with join operations for database tables will find the MapForce join component especially intuitive. A join operation in MapForce can even combine two different data formats and produce output in a new format altogether.

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