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.
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.
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.
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.
There are situations, especially when encountering loosely structured data, where you may want to map and transform structural components of a data stream along with content. MapForce 2017 includes a new feature to dynamically access node names of XML elements, attributes, or text file columns such as the contents of CSV files, to target components.
Dynamic access to node names allows creation on the fly of target elements and attributes whose names do not need to be known beforehand or specifically identified in the data mapping. This feature lets you create much more generic, flexible, and reusable mappings that require less manual intervention if data models evolve.
I’m excited to reveal some details of today’s Release 2 of Altova MissionKit 2016 desktop developer tools and software products!
v2016r2 introduces over 20 new features and updates to the Altova product line – but that’s a lot to cover in one blog post. Let’s take a look at the top five that are sure to blow your socks off.
Altova MapForce includes powerful mapping components that correspond to design patterns for data transformation requirements. Analyzing a data mapping challenge up front and following a few straightforward guidelines can uncover data mapping patterns that help simplify creation of the mapping design and lead to an optimal solution. The MapForce Examples project provides sample mapping files and data sets that illustrate many common data mapping patterns. Reviewing these examples and executing them with the MapForce Built-in Execution Engine is another good way to help select the best pattern for your own project.