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How to View Workflow Automation Reports on FlowForce Server


Altova FlowForce Server is a high-performance workflow engine for automating enterprise-level data processing, data integration, and ETL jobs.

FlowForce Server includes a helpful web interface for managing and monitoring all aspects of data processing jobs, including in-depth logging functionality and a complete visual dashboard. Let’s take a look at how you can take advantage of FlowForce charts and statistics to monitor the progress of FlowForce Server jobs, as well as performance of the server itself, in great detail.

Article about FlowForce Server
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New XML Grid and More in v2021r2


In the latest release of Altova desktop developer tools and server software products, we’re introducing a completely rebuilt XML Grid View, support for XSLT3 for XML data mapping, statistics and charts for monitoring FlowForce Server, and much more. Let’s take a look at the highlights of Altova Software Version 2021 Release 2. 

New features in Altova v2021r2
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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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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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Handling HTTP Errors in Web Service Data Mappings


Data integration projects that include information from external Web services may be vulnerable to HTTP errors when retrieving remote data. When data mappings run under automated control it’s especially important to detect and report errors even if errors only occur very rarely.

A MapForce data mapping can include Web service calls and output the result directly to a file or database, or combine it with other inputs for further processing. Regardless of the final output, an HTTP Web service error encountered in a REST Web service request puts the mapping at risk.

MapForce includes features for handling HTTP errors instead of simply aborting execution of a mapping. Developers can configure the body of a REST Web service call to handle and report exceptions based on the HTTP status code returned.

Let’s look at an example.

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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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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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