Posts

Database Mapping with Database Exception Handling


Critical business processes depend on reliable data and database administrators and other data analysts want to be confident in the integrity of information stored in database tables. During automated ETL (Extract Transform Load) operations or other database import tasks, invalid data might be encountered that jeopardizes success of the procedure. Altova MapForce includes database exception handling to roll back the affected data when an error occurs and optionally proceed with the rest of a database mapping.

For instance, an error in a single record need not prevent execution of a mapping from continuing, such as when certain database constraints prevent the mapping from inserting or updating invalid data.

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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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Get Sharp with Altova’s Latest Release


Altova Software Version 2019 introduces over 20 new features to help you sharpen your development game – starting with support for high-res monitors in both XMLSpy and UModel. There are also tools for working with new standards and database versions across the product line, the ability to map and convert data in Google Protocol Buffers format, and much more. Let’s take a look at the highlights.

Altova Version 2019

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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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The Only JSON Dev Tool You’ll Need


While XMLSpy might not be the first tool developers think of when they’ve got a JSON development task, XMLSpy includes comprehensive support for working with JSON, JSON Schema, and related technologies.

Over the past few product releases, we’ve added intelligent functionality for editing and converting JSON and JSON5 data to the product. We’ve completed the circle with one-click conversion between XML Schemas and JSON Schemas, as well as sample instance generation and JSON Schema documentation generation. And, most recently, we’ve added support for processing JSON with XSLT,  XPath, and XQuery.

Let’s walk through some common examples demonstrating this functionality – and see how these time-saving tools make XMLSpy the only JSON development tool you’ll need.

Developer using JSON tool

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