Database Tracing to Log Changes Made by a Data Mapping Project


Database administrators and other data professionals often want to maintain a record of changes in critical databases, especially when updates are made by automated scripts or other operations. Database tracing lets administrators track critical changes or anomalies, and help recover from errors. Altova MapForce supports database tracing for all popular relational databases to log the changes made by a data mapping project to the database when the mapping runs.

When tracing is enabled, events such as database insert or update actions, or errors, are logged in an XML file that you can later analyze or process further in an automated way.

Database tracing can be enabled at the database component, table, stored procedure, or database field level. You can choose to trace all messages or only errors, or you can disable tracing completely.

In addition to tracing errors that occur during the execution of a mapping to a target database, MapForce also enables database transaction handling to roll back the affected part of the database data when an error occurs, then optionally proceed with the rest of the mapping.

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Mapping Structured Data with Enhanced Node Functions


We’ve reported previously on support for node functions that simplify mapping structured data by eliminating need to copy-paste a function multiple times into a mapping. Repeating the same function unnecessarily clutters the mapping layout and makes the data mapping more difficult to understand or revise.

Now in MapForce 2019, additional filters are available for defining node functions. These new parameters allow developers to apply functions and default values to specific nodes based on custom-defined criteria. For example, you can apply a node function based on node metadata such as the node name, node length, precision of the node’s data type, customized node annotations, and more.

Let’s look at a mapping with enhanced node functions.

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Job Distribution on FlowForce Server


FlowForce Server is Altova’s high-performance engine for automating workflows of XML processing, data integration, report generation, and more. It integrates with other Altova server software products to automate their functions, such as executing complex data integration processes, including ETL projects, designed in MapForce; running  StyleVision report generation jobs; or validating XML, XBRL, or JSON files with RaptorXML Server.

Starting with Version 2019, FlowForce Server offers new options for distributed execution and load balancing to improve availability and performance. Let’s take a look at how configuring multiple FlowForce Servers to run as a cluster can help improve data throughput and provide redundancy.

Job distribution for high availability on FlowForce Server

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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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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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