Posts

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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MapForce Tutorial (Video)


Altova MapForce is an any-to-any data transformation, conversion, and ETL tool for integrating data.

A graphical data mapping tool, MapForce has an intuitive drag-and-drop interface that lets you easily convert data between any two formats, such as XML, JSON, relational databases, EDI, and more. It also features an extensive library of conversion functions that can be chained together to form custom functions that can be reused throughout your projects.

Data translated by MapForce can be pulled to or pushed from any relational database and all data management products, and it can be adapted to customize in-house data management solutions.

The MapForce tutorial video below covers all major features offered by the data integration tool and shows example mappings between several different types of files.

You can try MapForce yourself with a free, 30-day trial.

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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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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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A Typical MapForce Server Use Case


Envision a manufacturing company that controls costs by exploiting a just-in-time assembly process with a very low supply of parts inventory on hand. New customer orders are logged in a sales database and at the end of every day the components needed to assemble that day’s sales are tabulated.

The IT department runs a SQL query to identify the required parts and transforms the list into a purchase order in JSON format to be transmitted to the supply chain.

Sound familiar? Our recent blog series on JSON tools and JSON data mapping were based on this real-life scenario. In this post we describe a MapForce Server use case that automates the repetitive task of generating each day’s purchase order.

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