In the fast-paced world of modern application development, APIs are the glue that connects systems, services, and devices. REST APIs, in particular, are widely used for their simplicity, scalability, and flexibility. But as APIs grow in complexity and scale, managing them effectively becomes a challenge. That’s where the OpenAPI Specification (OAS) comes in.
OpenAPI offers a standardized way to describe RESTful APIs in a machine- and human-readable format. This not only enhances collaboration among development teams but also streamlines the entire API lifecycle, from design to documentation, integration, and testing. And with powerful tools like Altova XMLSpy and Altova MapForce, working with OpenAPI becomes faster, more visual, and far more efficient.
One of the benefits of using Shopify as an ecommerce platform is that it provides robust inventory management features. However, maintaining accurate inventory records in real-time can be challenging, especially for merchants who rely on manual data entry during inventory audits or restocking.
Handheld barcode scanners are a great solution for increasing speed and accuracy during inventory checks, but storeowners often lack a seamless way to connect scanner data directly to the Shopify system. Without proper integration, staff may still need to manually transfer scanned data into Shopify, negating some of the efficiency gains that barcode scanners are meant to provide.
Let’s take a look at a real-world scenario for building a mobile app with barcode scanning functionality that integrates directly with Shopify for real-time inventory management.
Shopify is a massively popular ecommerce platform in wide use by retail businesses large and small. Though Shopify provides easy-to-use tools for setting up and running an online storefront, managing the vast amount of data behind the scenes, such as product catalogs, customer information, order records, and inventory, can quickly become complex.
Businesses often need to integrate Shopify data with backend databases, ERP systems, CRMs, data warehouses, or other platforms to streamline operations, perform deeper analytics, or support automation workflows.
This is where a data mapping tool with Shopify support becomes essential, allowing businesses to transform, map, and move data between Shopify and other systems efficiently and accurately.
Let’s look at an example of a common scenario – extracting product data from Shopify to a CSV file – using visual tools in MapForce.
With Version 2025 Release 2, we’ve expanded functionality for working with YAML, OpenAPI, and XBRL, while also introducing new support for Shopify data integration.
Our ongoing commitment to integrating and enhancing support for popular technologies means developers have the functionality they need to stay in step with evolving industry standards—using tools they already know and trust.
Let’s take a look at the highlights in this release.
ETL—extract, transform, load—is the backbone of modern data integration. While most technical professionals understand the basics, the real challenge lies in designing efficient, scalable ETL processes that handle complex data transformations while maintaining performance and accuracy.
In our latest video series, we break down how ETL works, common challenges with defining ETL workflows, and how graphical tools like Altova MapForce can help. We’ll walk through demos of real-world scenarios such as transforming and loading CSV reports to a SQL database, as well as implementing scalable automation.
Whether you’re optimizing an existing process or researching new ETL tools, this series covers all the bases.
ETL processes span a wide spectrum of complexity, from straightforward tasks like a one-to-one mapping of an API payload to a database, to highly intricate scenarios requiring extensive data filtering, transformation, and manipulation.
Altova MapForce can tackle this full range of ETL tasks.
This video tutorial explores a common ETL scenario:
Extract CSV data received in multiple reports
Transform and filter the data
Load the transformed data to a target SQL database
This particular transformation is somewhat complex because the CSV data is in a wide format, with separate columns corresponding to each of several years. Part of our transformation will be to melt or pivot the data to the long format more in line with how data is stored in a relational database.
This way, each year becomes a value in a single column, and its corresponding data is moved to a new column, resulting in more rows but fewer columns. This long format is also more readily consumed by common analytical and BI tools down the line.
In addition, we will filter unwanted data and round up long decimals before writing the data to the database.
Though making this type of transformation seems like a complicated problem, it’s easy using MapForce ETL tools that include drag-and-drop data mapping, dynamic node names, and built-in functions.
The example in this video a CSV to database ETL scenario, but MapForce supports a wide variety of additional data formats including XML, JSON, PDF, Excel, EDI, and XBRL. All popular SQL and NoSQL databases are also supported as the source or target of any data mapping.
MapForce is available for a free, 30-day trial. Now account or credit card are needed – so you can just get to work trying this ETL functionality for yourself.
Up next: Check out the previous video in our series, ETL Basics.
ETL processes are increasingly required in modern enterprises as organizations receive data in diverse formats that must be transformed and loaded into target databases or business systems. ETL projects range from simple to highly complex, depending on the specific requirements.
A common example of a straightforward ETL process involves extracting CSV data from incoming files, mapping the data structure, applying basic transformations to align with the target schema, deduping records, and then finally loading the processed data into a SQL database.
Whether an ETL project is basic with just a one-to-one mapping or more complex with sophisticated data processing requirements, developers need tools that can handle the scope of complexity without a huge learning curve – or price tag. That’s where MapForce comes in.
As part of our series on defining ETL pipelines, this video walks you through this process of extracting data in CSV documents, transforming it using data processing functions, and then configuring how it will be written to the target system.
Though this example focuses on CSV, it’s easy to define data mapping projects in MapForce for any combination of data formats. Benefits of MapForce as an ETL tool include:
Graphical, drag-and-drop data mapping
Extensible library of data processing functions
Support for all major SQL and NoSQL databases
Support for CSV, XML, JSON, PDF, XBRL, and other data sources
Instant output with affordable ETL automation
Watch the video now:
To continue learning about defining more complex ETL pipelines, watch the next video in the series.
XBRL (eXtensible Business Reporting Language) is an open, XML-based standard for the electronic submission of business and financial data. Though XBRL specifies what data must be reported and provides a standardized way of doing so, companies and regulatory agencies need a way to ensure the quality of data that is submitted. One approach is by using business rule validation, and XULE is one method that is growing in popularity in 2025.
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.
Docker has revolutionized how developers build, ship, and run applications by offering a lightweight, portable solution for containerizing software. Containers package applications with all their dependencies, ensuring they run consistently across different environments, whether on a developer’s machine, a test server, or in the cloud. Compared to traditional virtual machines, Docker containers are faster to start, consume fewer resources, and make scaling applications easier. This efficiency has made Docker a go-to tool for software development and deployment.
Running Altova Server Software in Docker brings these benefits to enterprise-level data processing and automation tasks. By containerizing tools like FlowForce Server, MapForce Server, or RaptorXML Server, developers get a highly portable, scalable, and efficient solution for managing complex workflows as well as automating data transformations, validation, and report generation.
To make getting started even easier, Altova offers an open-source project for automating the set-up process in a Docker environment.
PDF documents are used at many stages of modern business workflows, often serving as the format of choice for invoices, reports, legal contracts, and other critical documents. While PDFs are ideal for preserving content integrity and a particular visual layout, their structure makes automated data extraction challenging. For organizations engaged in data integration and ETL, unlocking information contained in PDFs is a necessity—and this is where the MapForce PDF Extractor comes in.
The MapForce PDF Extractor includes multiple tools for visually defining extraction rules to map PDF data to other formats. One that is particularly useful for zeroing in on specific content is text search. Here’s how it works – including a video demo.
The European Banking Authority (EBA) requires banks to use the XBRL data format for their filings. XBRL is a standardized format that makes it easier to collect, analyze, and compare data from different financial institutions. By adhering to the EBA XBRL taxonomy, banks submit data that is consistent and easy to validate, which simplifies the reporting process for both filing institutions and regulators. This streamlined approach improves reporting accuracy and helps the EBA maintain better oversight of the European banking sector.
In addition to required validations in the EBA XBRL Taxonomy, the EBA recommends a set of optional data quality checks that allow reporting organizations to further improve data accuracy and compliance with regulatory requirements. These extra validations help filing companies improve the accuracy and integrity of their reported data – but only if the checks are supported by their XBRL reporting tools.
As part of its comprehensive XBRL support, Altova can run the optional EBA XBRL data quality checks in multiple products. Let’s see how it works.
With YAML becoming increasingly common in configuration files and data exchange, it makes sense for developers to seek out specialized YAML editors to add to their everyday toolkit.
Although YAML is simple, its strict reliance on indentation and formatting can make it prone to errors. Intelligent YAML editors provide features that can make coding faster while minimizing these issues. XMLSpy includes an intelligent, text-based YAML editor as well as a unique YAML Grid view. YAML Grid represents the document structure in a visual way that makes it easier to understand—while unlocking additional functionality for efficient editing.
The latest version of the Altova product line introduces a host of new features and functionality. Customers now have access to new, visual YAML editing tools, improved options for extracting PDF data for mapping to other formats, updated SQL and NoSQL data integration support, and more.
Whether working with Altova developer tools, server software products, or XBRL add-ins for Excel, this release has something for everyone. Here’s a look at the highlights.
Handheld barcode scanners are indispensable in many sectors today, from retail and logistics to healthcare, shipping, and manufacturing. These devices play a critical role in capturing real-time data, such as inventory updates, order tracking, and customer details. However, integrating scanned data seamlessly into backend systems is just as important as collecting it. Barcode scanner apps can bridge the the gap with the ability to transfer scanned information to databases and business systems.
While the need to integrate barcode scanner data into mobile apps is a common requirement, building scanning apps is often a challenge. Traditional development methods require specialized expertise and can take too long, especially in industries that demand rapid deployment.
Support for connecting to barcode scanners in Altova MobileTogether changes all of that. With this low-code app development framework, you can create, test, and deploy barcode-scanning solutions in a fraction of the time compared to conventional coding approaches.
The latest version of Altova MobileTogether introduces frequently requested support for building apps that connect to mobile barcode scanners. This addition makes the low-code development framework even more useful for creating solutions across industries that rely on barcodes and QR codes to update data in real time.
Version 10.0 also includes a new Styles Inspector in the app simulator, new controls, options for boosting flexibility and performance, and more.
In programming, modularization is the practice of dividing functionality into separate, independent modules. Modularization in app development is an efficient way of organizing app components and enabling collaboration within development teams. A modular approach also helps make testing, debugging, and maintenance of the app easier and more straightforward.
MobileTogether offers some classic – and some unique – approaches to modularization.
XQuery Update Facility is an extension of the XQuery language that allows you to make changes in an XML document using “update expressions” that insert, delete, replace, or rename nodes. This extension provides a convenient way to make intelligent updates to XML documents, and XMLSpy has a unique implementation that makes it even easier. Let’s see how it works.
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.
MapForce also includes additional filters are available for defining node functions. These 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.
YAML is increasing in popularity due to its combination of human readability, simplicity, and versatility. Often used in configuration files and for data serialization, YAML can be used alongside JSON and XML in modern systems. It makes sense, then, for developers to choose an IDE that supports all three standards.
New YAML tools in XMLSpy add to comprehensive support for XML and JSON development, giving users the flexibility to choose the most suitable serialization format for their specific use cases and preferences.
Let’s take a look at YAML support in the XML and JSON editor.
The latest release in Altova’s line of desktop developer tools and server software products includes support for new industry standards, updated database support, and performance optimizations.
With each new product version, we aim to provide customers with a mix of developer-requested features, support for emerging standards, and performance improvements. Version 2024r2 is no different, with tools introduced for working with YAML, FORTRAS EDI, and XBRL Report Packages as well as multiple performance and usability enhancements across the product line.
Configuring MobileTogether Server to work properly on your network will require some changes to be made. MobileTogether Server is designed to sit within your network’s DMZ, and enabling it to accept connections from clients both inside and outside your network will require your network administrator to open a collection of ports.
This video tutorial will walk you through the ports required to make MobileTogether function. It provides you with a baseline setup that will work inside most corporate networks. Please note, however, that every network is different and some configuration changes may be required. To support this, every port MobileTogether Server uses can be customized; all the ports listed in this tutorial are default and can be changed.
The image below outlines the network ports required by Altova LicenseServer to properly validate files.
Clients will need to be able to connect both internally and externally. We recommend using the default MobileTogether ports, and remapping them to 80 and 443 at each of your firewalls. This is discussed in greater detail in the video.
Finally, a set of ports will need to be opened for administrator purposes. These ports should be limited to your internal network only.
Using a Reverse Proxy Server
When setting up a MobileTogether Server for a public-facing app that will be accessed via URL in a web browser (in addition to from MobileTogether client apps), it may be helpful to hide the precise URL that is being used on the server to start the solution.
The best ideas for new features and other improvements to existing software apps often come from enthusiastic users. Implementing new features creates opportunities for refactoring mobile apps. After several years using the MobileTogether Solar Data Tracking app to monitor the performance of a rooftop solar system, my friend Casey had a suggestion.
The app displayed one page of charts and tables to report production by month over a year and another page to report daily production for the last 30 days. Navigation buttons on each page enabled easy switching between views. Casey’s suggestion was to revise the app to place all the charts and tables on a single page. Over time mobile users have become accustomed to mobile apps that present data in long pages that scroll with a quick one-finger swipe motion. A one-page app would feel smooth and more up-to-date.
Visual, no code tools like Altova RecordsManager have revolutionized the field of data-centric app creation, making it faster and more accessible. But now imagine expressing your database vision in a single sentence, and having it created automatically – including not just the database structure and tables, but forms and reports as well. That’s exactly what the new AI Assistant in RecordsManager does.
With a single AI prompt, users of all skill levels can turn their ideas into functional database solutions without any coding or database design expertise required. RecordsManager lets you skip the manual work of database design so you can focus more on the higher-level aspects of your project.
As IoT adoption continues, smart devices are driving efficient automation in our homes, in offices, and at scale in myriad industrial applications. Communication protocols play an increasingly pivotal role in ensuring seamless connectivity between devices used in these scenarios. One such protocol that has gained widespread adoption for its efficiency and lightweight nature is MQTT.
MQTT’s flexibility allows app developers to implement it in various scenarios, from small-scale projects to enterprise-level applications. Whether it’s a simple office automation system or a complex industrial network, MQTT provides reliable, efficient communication between smart devices in real time.
Altova MobileTogether includes comprehensive support for building MQTT-enabled apps for iOS, Android, and Windows devices. Its low-code, RMAD approach to cross-platform app development gets those apps out the door in record time.
Version 9.0 of MobileTogether, Altova’s low code app development framework, is now available with new features including comprehensive MQTT support, support for new gestures, drawing barcodes and QR codes, dark mode, and much more.
A common requirement in data processing is batch data mapping, especially in the context of data transformation and integration. It involves converting data in batches rather than processing individual data points one at a time. Batch data mapping is often required in data integration scenarios where input from multiple sources needs to be aligned or transformed together. Two common scenarios are “batch to batch” and “batch to one.”
In our latest series of MapForce demo videos, we explore these common data mapping challenges.
BATCH TO BATCH DATA MAPPING
Batch to batch data mapping is helpful in scenarios where you have data updates or changes coming in batches, and you need to synchronize or transform these batches together. This could be to convert them to a different format, perform some type of sort or calculation, or a combination of these.
In this demo, we create a data mapping project that reads files from a directory and uses wildcards to set up a mapping that will process data from multiple files at once. Then, we explore another option for defining batch conversion using dynamic file names supplied by the mapping. This demo also shows how to add calculations and comments to your data mapping projects.
While this video highlights a batch to batch transformation of JSON files to XML files, MapForce supports conversion and transformation for any combination of XML, JSON, PDF, database, text, Protobuf, Excel, XBRL, and so on, for advanced data integration and ETL processes.
BATCH TO ONE DATA TRANSFORMATION
Batch to one data transformation is another common requirement, for example, when you want to merge or combine multiple files into a single consolidated document and perform some data transformation, conversion, or calculation operations in between.
This example also explores multiple approaches to defining the batch process, which will be applicable in different scenarios:
Specifying a collection of files in the same directory using a wildcard
Selecting batch files based on a list of file names stored in a different directory
This demo also shows how to sort the data merged from multiple files before writing it to the target.
After watching these quick demos, you can download a free trial of MapForce to try batch mapping, conversion, and transformation for yourself.
MORE MAPFORCE DEMOS
If you liked these videos, check out the rest of the MapForce demo series, which covers everything from mapping XML and JSON to databases to configuring data processing functions and extracting data from PDF documents.
AI is a great productivity booster for IT projects, and working with databases is one area where AI is really making inroads for improving efficiency. By leveraging AI in database tools, DBAs and database developers of any skill level can save time and effort with AI-generated SQL scripts and sample data, for instance, as well as query optimization and troubleshooting.
Altova offers an integrated AI Assistant in DatabaseSpy to help with SQL script creation, data modeling, SQL and error explanations, and even SQL pretty-printing. This makes the multi-database tool, which supports all major databases in a single UI, even more useful.
The explosion of AI tools has made a significant impact on the field of software development – not by replacing software engineers as some have predicted – but by actually increasing their value by freeing them to focus on higher-level tasks. By automating low-level code generation, for instance, AI increases development speed and opens the doors to deeper innovation.
To give developers the AI tools they need to realize these productivity and creativity gains, Altova has integrated AI functionality in XMLSpy for XML and JSON editing tasks.
MapForce, Altova’s award-winning data mapping tool, includes support for PDF input in data integration and ETL workflows. The MapForce PDF Extractor makes it easy to define rules for extracting PDF data in a structured format to make it available for mapping to other popular formats like Excel, XML, JSON, databases, and more.
Version 2024 of Altova Software introduces brand new AI Assistants in multiple products as well as long-awaited support for PDF data integration in MapForce. Other features include Markdown editing support, split output preview for business report creation, support for new XBRL standards, and much more.
The advantages of JSON as a lightweight, human-readable, interoperable data format have led to its widespread adoption in various domains, including web development, mobile app development, and backend services. Many programming libraries and frameworks provide built-in support for JSON parsing and serialization.
That said, most applications still benefit from or require validation of client-submitted data. Enter the JSON Schema spec, which lets you describe the structure of JSON data for a particular application, for both documentation and validation purposes.
Though JSON Schema code is by design human-readable, building a complex schema with nested and repeating sections in a text-only editor becomes time consuming and error-prone quickly. Let’s look at five ways a graphical editor is a must-have for JSON Schema development.
One of the most common examples of AI in our everyday lives is facial recognition. Facial recognition is the process of identifying or verifying a person’s identity based on their face. Facial recognition is used in many applications, such as unlocking our phones with FaceID, tagging our friends on social media platforms like Facebook, and checking in at airports or hotels with biometric scanners. Facial recognition can make our lives more convenient and secure, but it can also raise some privacy and ethical concerns. For instance, how can we ensure that our facial data is not misused or stolen by hackers or malicious actors? How can we prevent facial recognition from being used for surveillance or discrimination? How can we ensure that facial recognition is accurate and fair, and does not have any biases or errors?
The paragraph above was generated by ChatGPT in response to my request to describe the benefits and risks of artificial intelligence and include a real-life example. It’s interesting that ChatGPT chose FaceID as the example, since FaceID is simply one variation of image analysis and AI-powered image classification offers potential to automate many real-world tasks.
One common use-case is a product catalog, wherein a company manages product information provided by many different manufacturers. A product loaded into that database may have a name that does not necessarily include a precise description of the item. For instance, wellington is a boot, fedora is a hat, a mongoose is a bicycle, and a yellow watermelon shiny needlefish is a fishing lure. We can make use of AI-powered image classification using the Microsoft Azure Cognitive Services Computer Vision API to address this problem. The Computer Vision Service takes the image data or URL as its input and returns information about the content. One service generates image classification tags based on a training set of recognizable objects, living beings, scenery, and actions that the Azure AI has been trained on. These tags allow us to categorize products in the database accordingly and may even correspond to search terms a user might provide to find products in the catalog.
Automated sentiment analysis of text, such as user reviews, has historically been a challenge. Because of the myriad intricacies of natural language, systems faced difficulties in analyzing context and nuances. This required an inordinate amount of manual work to overcome.
One of the many useful capabilities of modern AI systems that are based on large language models (LLMs) such as OpenAI’s GPT-4 is that they are very good at sentiment analysis of natural text inputs. We can use that capability to build a very efficient database solution in MapForce that, for example, goes through all the new incoming records in a support database and automatically determines whether a particular support request or other customer feedback is positive, negative, constitutes a bug report, or should be considered as a feature request.
Data mapping plays a vital role in modern data-driven organizations, enabling efficient data management and integration. Altova MapForce is a powerful, graphical data mapping tool that supports endless data transformation scenarios, including one-to-one, one-to-many, many-to-one, and chained data conversion.
While there are applications for each of these approaches, chained data mapping is especially helpful for complex data processing tasks where multiple stages of data manipulation are required. Here’s a look at the benefits of a chained data conversion approach – and a video of how MapForce makes the process easy and straightforward.
Altova MapForce offers a powerful, yet easy-to-use, approach to converting and transforming data. Whether you need to write XML to a database, convert JSON to EDI, or map Excel to multiple different data formats, MapForce has you covered.
From simple one-to-one conversions to complex ETL scenarios, the MapForce approach is to represent data structures as graphical components. To associate fields, drag and drop connecting lines. A comprehensive library of data filters and functions is available for transforming data before writing it to the target.
We have recently revamped our series of Introduction to MapForce videos. Each short how-to gives a demo of a common MapForce scenario.
Start at the beginning to learn how MapForce works:
You can follow along with the examples in these how-to videos by downloading a free, 30-day trial of MapForce. Check back for new MapForce videos, which are added often.
Code editors with a graphical option in addition to the traditional text view are great for developers working with XML or JSON. Graphical editors like Grid View in XMLSpy provide a visual representation of the JSON or XML structure, making it easier to understand, navigate, and edit complex data hierarchies.
XMLSpy introduced Grid View years ago and has been improving on it ever since. The most recent update includes the ability to split the editing pane horizontally or vertically, which is especially handy when working with large documents.
To meet current mandates, ESEF (European Single Electronic Format) reports must be filed in XBRL. To accelerate the process, regulated companies can take advantage of intelligent software tools to fill the reports with data. One such tool from Altova allows organizations to create valid ESEF XBRL directly in Excel, where finance pros are already comfortable working. This allows them to create reports for filing without having to learn the intricacies of XBRL syntax.
Unlike some other XBRL filing mandates, ESEF requires filing companies to create their own extension taxonomy, which defines the entity-specific data rules for their report. To help customers with this step of the filing process, the Altova ESEF XBRL add-in for Excel includes a visual extension taxonomy designer that makes it a complete start-to-finish ESEF reporting solution.
EDI (electronic data interchange) messages are used in a variety of industries for transmitting business information such as invoices, shipping notices, and purchase orders—which were traditionally recorded on paper—electronically. EDI transactions are structured according to standards that describe the format of each message. Adherence to a standard format enables the messages to be transmitted electronically between the computer systems of trading partners without human interaction.
Different industries take advantage of specialized EDI formats that are relevant to their business, and one such popular format is VDA EDI, which is used by the German automotive industry.
To work with VDA messages efficiently, companies often need to transform them to other formats, for instance, for storage in a backend database, or convert them to other EDI message formats for compatibility among systems. Altova MapForce makes this easy, with support for VDA as well as several other popular EDI standards. Let’s see how it works.
The latest release of Altova software includes ongoing enhancements for graphical XML and JSON editing in XMLSpy Grid View, new tools for working with XPath/XQuery, support for integrating VDA EDI data in MapForce, and much more.
Here’s a quick overview of all the new functionality being introduced.
Data-driven solutions like database and enterprise apps rely on connection to, and interaction with, backend databases. Backend relational databases, however, store data in tables that reflect complex data relationships. This provides numerous advantages for effective data management and data integrity but can make it difficult to access and work with the data stored therein in new ways. App developers need to have a comprehensive understanding of database design principles and the SQL query language just to get started.
In contrast, real world data relationships most often represent parent-child relationships or even deeper hierarchical structure. As such, working with hierarchical data where relationships can be visualized in a tree structure can be much simpler and more flexible, leading to faster development. This approach is also more accessible to developers without extensive SQL expertise.
To make building apps that connect to the backend relational databases that are ubiquitous in today’s enterprise easier, faster, and available to a wider range of developers, Altova MobileTogether takes an entirely unique approach. Its visual Database Wizard lets developers easily build a query that returns hierarchical data, work with that data in the app, and then easily save the data back in hierarchical form, letting MobileTogether take care of normalizing the data and writing it back to the corresponding linked tables. Let’s take a look at how it works.
Enterprise database apps are increasing in prevalence due to their advantages for enabling access to—and easy management of—the ever-growing amount of critical data business users need to work with on a day-to-day basis. Unlike other types of business productivity apps, database apps must include measures for managing different levels of user access to maintain the security and integrity of the enterprise data they expose.
This can include managing read-only and editing access rights or restrictions on access to certain types of data. While it is essential to ensure that only authorized personnel have access to confidential data, levels of permissions often vary throughout an organization. Apps built using Altova RecordsManager include comprehensive tools for managing role-based access to database data that can reflect these complicated relationships that exist within an organization.
Let’s take a look at how RecordsManager makes it easy for app administrators to manage complex role-based permissions with visual tools.
The low code approach to app creation has been very effective at decreasing the complexity and learning-curve associated with building custom apps in response to quickly changing business requirements. Low code tools do the heavy lifting for developers and system administrators, freeing them to focus on business needs and rules rather than writing complex code.
To be truly successful, any low code approach must still include the ability to configure sophisticated app behavior in response to user input. This can be a challenge to implement with visual tools.
Altova RecordsManager offers a low code approach to creating database apps, with a built-in scripting editor that is purely visual. This offers the best of both worlds: easy app creation and sophisticated functionality customized for data-centric applications. Let’s see how it works.
We’ve updated our demo series on building an app that connects to a backend database using MobileTogether. The low-code approach to app development in MobileTogether extends to database connectivity: the Database Wizard includes a visual SQL statement editor that makes it easier than ever to connect to and work with backend database data in your apps.
This how-to video series takes you through the process of developing a sample Books Catalog app with rich functionality for searching, adding new database records, working with images, and more.
Altova releases new versions of its app development framework multiple times a year to introduce new features added in response to customer requests and feedback, as well as to add support for newer OS and database versions as they become available.
The latest release of MobileTogether and RecordsManager introduces important new functionality for building low-code and no-code apps.
Like other regulatory agencies around the world, the European Banking Authority (EBA) has standardized on XBRL for the transmission of data submitted by filing entities. Benefits of using XBRL include increased accuracy and efficiency of supervisory practices and risk identification. The use of XBRL benefits filing organizations as well, because the now-standardized data can be easily validated and then used further for automated report generation and other common requirements.
However, the challenge lies in getting backend data into a valid XBRL format according to the EBA Taxonomy, especially since the employees recording the data are generally financial professionals and not familiar with XBRL syntax.
Let’s take a look at how EBA reporting tools can make it easier.
Software developers and other data professionals often need to transform data from one format to another. These transformations can be simple one-to-one conversions or may require more complex manipulation. For instance, relationships must be generated when importing flat CSV files into a database, or source data may need to be split for the target, as in full name vs. first, middle, last, and optional suffix. Validating data transformation is critical to prevent data loss or corruption.
In an earlier post on Web service data integration, we combined a string value for GMT time with a numeric offset in seconds to generate the local time for weather forecasts. We created a user function that performed all the steps required to complete this operation. MapForce includes a powerful interactive data mapping debugger that can easily trace and validate this transformation. Let’s take a look at how it works.
Developers working with XML often need to deal with multiple DTDs and XSDs that define industry-standard vocabularies. Whether it’s DITA for technical writing, HL7 for healthcare data, CbCR for financial reporting or any number of examples, it becomes a challenge to manage the various schemas—and numerous versions thereof—on a day-to-day basis.
For flexibility and convenience, all Altova XML-enabled products include its XML Schema Manager. This provides a centralized utility that makes it easy to download and manage industry schemas for use across the product line. Let’s see how it works.
The ESEF acronym has been top of mind for finance professionals across the EU and UK since mid 2019, when an upcoming reporting mandate from the European Securities and Markets Authority (ESMA) was announced.
ESEF, which stands for European Single Electronic Format, is a digital financial reporting standard based on XBRL. As of early 2020, companies on EU regulated markets are mandated to prepare their annual reports in accordance with ESEF rules.
What exactly is ESEF compliance, and what does it take to meet reporting requirements? Let’s take a look at the basics and some tools that make it easy.
Long-time XMLSpy and MapForce customers may remember the fun drawings and depictions from Altova’s early ads and logos. Those dark spy images have given way to brighter colors and imagery over the years – but at the same time, developers have gravitated towards dark mode in their applications. Now, dark mode is finally available for XMLSpy and MapForce!
This fun option is being released alongside important support for additional standards and databases, a new tool for managing schemas across the product line, and a brand new product for building ESEF XBRL reports in Excel.
OpenAPI Tools for Developers
In the fast-paced world of modern application development, APIs are the glue that connects systems, services, and devices. REST APIs, in particular, are widely used for their simplicity, scalability, and flexibility. But as APIs grow in complexity and scale, managing them effectively becomes a challenge. That’s where the OpenAPI Specification (OAS) comes in.
OpenAPI offers a standardized way to describe RESTful APIs in a machine- and human-readable format. This not only enhances collaboration among development teams but also streamlines the entire API lifecycle, from design to documentation, integration, and testing. And with powerful tools like Altova XMLSpy and Altova MapForce, working with OpenAPI becomes faster, more visual, and far more efficient.
Updating Shopify Inventory from a Mobile App
One of the benefits of using Shopify as an ecommerce platform is that it provides robust inventory management features. However, maintaining accurate inventory records in real-time can be challenging, especially for merchants who rely on manual data entry during inventory audits or restocking.
Handheld barcode scanners are a great solution for increasing speed and accuracy during inventory checks, but storeowners often lack a seamless way to connect scanner data directly to the Shopify system. Without proper integration, staff may still need to manually transfer scanned data into Shopify, negating some of the efficiency gains that barcode scanners are meant to provide.
Let’s take a look at a real-world scenario for building a mobile app with barcode scanning functionality that integrates directly with Shopify for real-time inventory management.
Exporting Products from Shopify as CSV
Shopify is a massively popular ecommerce platform in wide use by retail businesses large and small. Though Shopify provides easy-to-use tools for setting up and running an online storefront, managing the vast amount of data behind the scenes, such as product catalogs, customer information, order records, and inventory, can quickly become complex.
Businesses often need to integrate Shopify data with backend databases, ERP systems, CRMs, data warehouses, or other platforms to streamline operations, perform deeper analytics, or support automation workflows.
This is where a data mapping tool with Shopify support becomes essential, allowing businesses to transform, map, and move data between Shopify and other systems efficiently and accurately.
Let’s look at an example of a common scenario – extracting product data from Shopify to a CSV file – using visual tools in MapForce.
New Tools for Shopify, OpenAPI, and More
With Version 2025 Release 2, we’ve expanded functionality for working with YAML, OpenAPI, and XBRL, while also introducing new support for Shopify data integration.
Our ongoing commitment to integrating and enhancing support for popular technologies means developers have the functionality they need to stay in step with evolving industry standards—using tools they already know and trust.
Let’s take a look at the highlights in this release.
What is ETL?
ETL—extract, transform, load—is the backbone of modern data integration. While most technical professionals understand the basics, the real challenge lies in designing efficient, scalable ETL processes that handle complex data transformations while maintaining performance and accuracy.
In our latest video series, we break down how ETL works, common challenges with defining ETL workflows, and how graphical tools like Altova MapForce can help. We’ll walk through demos of real-world scenarios such as transforming and loading CSV reports to a SQL database, as well as implementing scalable automation.
Whether you’re optimizing an existing process or researching new ETL tools, this series covers all the bases.
ETL Tutorial: Video
ETL processes span a wide spectrum of complexity, from straightforward tasks like a one-to-one mapping of an API payload to a database, to highly intricate scenarios requiring extensive data filtering, transformation, and manipulation.
Altova MapForce can tackle this full range of ETL tasks.
This video tutorial explores a common ETL scenario:
This particular transformation is somewhat complex because the CSV data is in a wide format, with separate columns corresponding to each of several years. Part of our transformation will be to melt or pivot the data to the long format more in line with how data is stored in a relational database.
This way, each year becomes a value in a single column, and its corresponding data is moved to a new column, resulting in more rows but fewer columns. This long format is also more readily consumed by common analytical and BI tools down the line.
In addition, we will filter unwanted data and round up long decimals before writing the data to the database.
Though making this type of transformation seems like a complicated problem, it’s easy using MapForce ETL tools that include drag-and-drop data mapping, dynamic node names, and built-in functions.
The example in this video a CSV to database ETL scenario, but MapForce supports a wide variety of additional data formats including XML, JSON, PDF, Excel, EDI, and XBRL. All popular SQL and NoSQL databases are also supported as the source or target of any data mapping.
MapForce is available for a free, 30-day trial. Now account or credit card are needed – so you can just get to work trying this ETL functionality for yourself.
Up next: Check out the previous video in our series, ETL Basics.
ETL Basics: CSV to Database in MapForce
ETL processes are increasingly required in modern enterprises as organizations receive data in diverse formats that must be transformed and loaded into target databases or business systems. ETL projects range from simple to highly complex, depending on the specific requirements.
A common example of a straightforward ETL process involves extracting CSV data from incoming files, mapping the data structure, applying basic transformations to align with the target schema, deduping records, and then finally loading the processed data into a SQL database.
Whether an ETL project is basic with just a one-to-one mapping or more complex with sophisticated data processing requirements, developers need tools that can handle the scope of complexity without a huge learning curve – or price tag. That’s where MapForce comes in.
As part of our series on defining ETL pipelines, this video walks you through this process of extracting data in CSV documents, transforming it using data processing functions, and then configuring how it will be written to the target system.
Though this example focuses on CSV, it’s easy to define data mapping projects in MapForce for any combination of data formats. Benefits of MapForce as an ETL tool include:
Watch the video now:
To continue learning about defining more complex ETL pipelines, watch the next video in the series.
Learn about XULE for XBRL
XBRL (eXtensible Business Reporting Language) is an open, XML-based standard for the electronic submission of business and financial data. Though XBRL specifies what data must be reported and provides a standardized way of doing so, companies and regulatory agencies need a way to ensure the quality of data that is submitted. One approach is by using business rule validation, and XULE is one method that is growing in popularity in 2025.
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.
How to Run Altova Server Software in Docker
Docker has revolutionized how developers build, ship, and run applications by offering a lightweight, portable solution for containerizing software. Containers package applications with all their dependencies, ensuring they run consistently across different environments, whether on a developer’s machine, a test server, or in the cloud. Compared to traditional virtual machines, Docker containers are faster to start, consume fewer resources, and make scaling applications easier. This efficiency has made Docker a go-to tool for software development and deployment.
Running Altova Server Software in Docker brings these benefits to enterprise-level data processing and automation tasks. By containerizing tools like FlowForce Server, MapForce Server, or RaptorXML Server, developers get a highly portable, scalable, and efficient solution for managing complex workflows as well as automating data transformations, validation, and report generation.
To make getting started even easier, Altova offers an open-source project for automating the set-up process in a Docker environment.
Text Search for Precise PDF Data Extraction
PDF documents are used at many stages of modern business workflows, often serving as the format of choice for invoices, reports, legal contracts, and other critical documents. While PDFs are ideal for preserving content integrity and a particular visual layout, their structure makes automated data extraction challenging. For organizations engaged in data integration and ETL, unlocking information contained in PDFs is a necessity—and this is where the MapForce PDF Extractor comes in.
The MapForce PDF Extractor includes multiple tools for visually defining extraction rules to map PDF data to other formats. One that is particularly useful for zeroing in on specific content is text search. Here’s how it works – including a video demo.
How to Implement EBA XBRL Data Quality Checks
The European Banking Authority (EBA) requires banks to use the XBRL data format for their filings. XBRL is a standardized format that makes it easier to collect, analyze, and compare data from different financial institutions. By adhering to the EBA XBRL taxonomy, banks submit data that is consistent and easy to validate, which simplifies the reporting process for both filing institutions and regulators. This streamlined approach improves reporting accuracy and helps the EBA maintain better oversight of the European banking sector.
In addition to required validations in the EBA XBRL Taxonomy, the EBA recommends a set of optional data quality checks that allow reporting organizations to further improve data accuracy and compliance with regulatory requirements. These extra validations help filing companies improve the accuracy and integrity of their reported data – but only if the checks are supported by their XBRL reporting tools.
As part of its comprehensive XBRL support, Altova can run the optional EBA XBRL data quality checks in multiple products. Let’s see how it works.
Easy Tools for YAML Editing
With YAML becoming increasingly common in configuration files and data exchange, it makes sense for developers to seek out specialized YAML editors to add to their everyday toolkit.
Although YAML is simple, its strict reliance on indentation and formatting can make it prone to errors. Intelligent YAML editors provide features that can make coding faster while minimizing these issues. XMLSpy includes an intelligent, text-based YAML editor as well as a unique YAML Grid view. YAML Grid represents the document structure in a visual way that makes it easier to understand—while unlocking additional functionality for efficient editing.
Let’s take a look at how it works.
Altova Version 2025 with YAML Grid and More
The latest version of the Altova product line introduces a host of new features and functionality. Customers now have access to new, visual YAML editing tools, improved options for extracting PDF data for mapping to other formats, updated SQL and NoSQL data integration support, and more.
Whether working with Altova developer tools, server software products, or XBRL add-ins for Excel, this release has something for everyone. Here’s a look at the highlights.
How to Build Apps for Barcode Scanners
Handheld barcode scanners are indispensable in many sectors today, from retail and logistics to healthcare, shipping, and manufacturing. These devices play a critical role in capturing real-time data, such as inventory updates, order tracking, and customer details. However, integrating scanned data seamlessly into backend systems is just as important as collecting it. Barcode scanner apps can bridge the the gap with the ability to transfer scanned information to databases and business systems.
While the need to integrate barcode scanner data into mobile apps is a common requirement, building scanning apps is often a challenge. Traditional development methods require specialized expertise and can take too long, especially in industries that demand rapid deployment.
Support for connecting to barcode scanners in Altova MobileTogether changes all of that. With this low-code app development framework, you can create, test, and deploy barcode-scanning solutions in a fraction of the time compared to conventional coding approaches.
New in MobileTogether 10.0
The latest version of Altova MobileTogether introduces frequently requested support for building apps that connect to mobile barcode scanners. This addition makes the low-code development framework even more useful for creating solutions across industries that rely on barcodes and QR codes to update data in real time.
Version 10.0 also includes a new Styles Inspector in the app simulator, new controls, options for boosting flexibility and performance, and more.
Here’s a look at the highlights.
Modularization for App Development
In programming, modularization is the practice of dividing functionality into separate, independent modules. Modularization in app development is an efficient way of organizing app components and enabling collaboration within development teams. A modular approach also helps make testing, debugging, and maintenance of the app easier and more straightforward.
MobileTogether offers some classic – and some unique – approaches to modularization.
Learn About XQuery Update Facility
XQuery Update Facility is an extension of the XQuery language that allows you to make changes in an XML document using “update expressions” that insert, delete, replace, or rename nodes. This extension provides a convenient way to make intelligent updates to XML documents, and XMLSpy has a unique implementation that makes it even easier. Let’s see how it works.
Read more…
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.
MapForce also includes additional filters are available for defining node functions. These 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.
Read more…
YAML Editing Tools
YAML is increasing in popularity due to its combination of human readability, simplicity, and versatility. Often used in configuration files and for data serialization, YAML can be used alongside JSON and XML in modern systems. It makes sense, then, for developers to choose an IDE that supports all three standards.
New YAML tools in XMLSpy add to comprehensive support for XML and JSON development, giving users the flexibility to choose the most suitable serialization format for their specific use cases and preferences.
Let’s take a look at YAML support in the XML and JSON editor.
Version 2024r2 Introduces Support for YAML, FORTRAS EDI, and More
The latest release in Altova’s line of desktop developer tools and server software products includes support for new industry standards, updated database support, and performance optimizations.
With each new product version, we aim to provide customers with a mix of developer-requested features, support for emerging standards, and performance improvements. Version 2024r2 is no different, with tools introduced for working with YAML, FORTRAS EDI, and XBRL Report Packages as well as multiple performance and usability enhancements across the product line.
Here’s a look at the highlights.
Configuring MobileTogether Server to Work With Your Network
Configuring MobileTogether Server to work properly on your network will require some changes to be made. MobileTogether Server is designed to sit within your network’s DMZ, and enabling it to accept connections from clients both inside and outside your network will require your network administrator to open a collection of ports.
This video tutorial will walk you through the ports required to make MobileTogether function. It provides you with a baseline setup that will work inside most corporate networks. Please note, however, that every network is different and some configuration changes may be required. To support this, every port MobileTogether Server uses can be customized; all the ports listed in this tutorial are default and can be changed.
The image below outlines the network ports required by Altova LicenseServer to properly validate files.
Clients will need to be able to connect both internally and externally. We recommend using the default MobileTogether ports, and remapping them to 80 and 443 at each of your firewalls. This is discussed in greater detail in the video.
Finally, a set of ports will need to be opened for administrator purposes. These ports should be limited to your internal network only.
Using a Reverse Proxy Server
When setting up a MobileTogether Server for a public-facing app that will be accessed via URL in a web browser (in addition to from MobileTogether client apps), it may be helpful to hide the precise URL that is being used on the server to start the solution.
Solution URLs follow this convention: https://server.name/run?d=/public/SolutionName. You can customize the URL to hide the “run?d…” portion by deploying a reverse proxy server in front of the MobileTogether Server.
Refactoring Mobile Apps
The best ideas for new features and other improvements to existing software apps often come from enthusiastic users. Implementing new features creates opportunities for refactoring mobile apps. After several years using the MobileTogether Solar Data Tracking app to monitor the performance of a rooftop solar system, my friend Casey had a suggestion.
The app displayed one page of charts and tables to report production by month over a year and another page to report daily production for the last 30 days. Navigation buttons on each page enabled easy switching between views. Casey’s suggestion was to revise the app to place all the charts and tables on a single page. Over time mobile users have become accustomed to mobile apps that present data in long pages that scroll with a quick one-finger swipe motion. A one-page app would feel smooth and more up-to-date.
AI Tools for Instant App Creation
Visual, no code tools like Altova RecordsManager have revolutionized the field of data-centric app creation, making it faster and more accessible. But now imagine expressing your database vision in a single sentence, and having it created automatically – including not just the database structure and tables, but forms and reports as well. That’s exactly what the new AI Assistant in RecordsManager does.
With a single AI prompt, users of all skill levels can turn their ideas into functional database solutions without any coding or database design expertise required. RecordsManager lets you skip the manual work of database design so you can focus more on the higher-level aspects of your project.
Let’s see how it works.
Build an MQTT-enabled App
As IoT adoption continues, smart devices are driving efficient automation in our homes, in offices, and at scale in myriad industrial applications. Communication protocols play an increasingly pivotal role in ensuring seamless connectivity between devices used in these scenarios. One such protocol that has gained widespread adoption for its efficiency and lightweight nature is MQTT.
MQTT’s flexibility allows app developers to implement it in various scenarios, from small-scale projects to enterprise-level applications. Whether it’s a simple office automation system or a complex industrial network, MQTT provides reliable, efficient communication between smart devices in real time.
Altova MobileTogether includes comprehensive support for building MQTT-enabled apps for iOS, Android, and Windows devices. Its low-code, RMAD approach to cross-platform app development gets those apps out the door in record time.
Let’s see how it works.
MobileTogether 9.0 with MQTT Support and More
Version 9.0 of MobileTogether, Altova’s low code app development framework, is now available with new features including comprehensive MQTT support, support for new gestures, drawing barcodes and QR codes, dark mode, and much more.
Here’s a look at all the highlights.
How to Create Batch Data Mapping Projects
A common requirement in data processing is batch data mapping, especially in the context of data transformation and integration. It involves converting data in batches rather than processing individual data points one at a time. Batch data mapping is often required in data integration scenarios where input from multiple sources needs to be aligned or transformed together. Two common scenarios are “batch to batch” and “batch to one.”
In our latest series of MapForce demo videos, we explore these common data mapping challenges.
BATCH TO BATCH DATA MAPPING
Batch to batch data mapping is helpful in scenarios where you have data updates or changes coming in batches, and you need to synchronize or transform these batches together. This could be to convert them to a different format, perform some type of sort or calculation, or a combination of these.
In this demo, we create a data mapping project that reads files from a directory and uses wildcards to set up a mapping that will process data from multiple files at once. Then, we explore another option for defining batch conversion using dynamic file names supplied by the mapping. This demo also shows how to add calculations and comments to your data mapping projects.
While this video highlights a batch to batch transformation of JSON files to XML files, MapForce supports conversion and transformation for any combination of XML, JSON, PDF, database, text, Protobuf, Excel, XBRL, and so on, for advanced data integration and ETL processes.
BATCH TO ONE DATA TRANSFORMATION
Batch to one data transformation is another common requirement, for example, when you want to merge or combine multiple files into a single consolidated document and perform some data transformation, conversion, or calculation operations in between.
This example also explores multiple approaches to defining the batch process, which will be applicable in different scenarios:
This demo also shows how to sort the data merged from multiple files before writing it to the target.
After watching these quick demos, you can download a free trial of MapForce to try batch mapping, conversion, and transformation for yourself.
MORE MAPFORCE DEMOS
If you liked these videos, check out the rest of the MapForce demo series, which covers everything from mapping XML and JSON to databases to configuring data processing functions and extracting data from PDF documents.
AI-Ready Database Tool
AI is a great productivity booster for IT projects, and working with databases is one area where AI is really making inroads for improving efficiency. By leveraging AI in database tools, DBAs and database developers of any skill level can save time and effort with AI-generated SQL scripts and sample data, for instance, as well as query optimization and troubleshooting.
Altova offers an integrated AI Assistant in DatabaseSpy to help with SQL script creation, data modeling, SQL and error explanations, and even SQL pretty-printing. This makes the multi-database tool, which supports all major databases in a single UI, even more useful.
Let’s take a look at how it works.
AI Tools for XML and JSON Development
The explosion of AI tools has made a significant impact on the field of software development – not by replacing software engineers as some have predicted – but by actually increasing their value by freeing them to focus on higher-level tasks. By automating low-level code generation, for instance, AI increases development speed and opens the doors to deeper innovation.
To give developers the AI tools they need to realize these productivity and creativity gains, Altova has integrated AI functionality in XMLSpy for XML and JSON editing tasks.
Here’s how the XMLSpy AI Assistant works.
Extract Data for PDF Mapping
MapForce, Altova’s award-winning data mapping tool, includes support for PDF input in data integration and ETL workflows. The MapForce PDF Extractor makes it easy to define rules for extracting PDF data in a structured format to make it available for mapping to other popular formats like Excel, XML, JSON, databases, and more.
Let’s take a look at how it works.
AI Integration & PDF Data Mapping in Version 2024
Version 2024 of Altova Software introduces brand new AI Assistants in multiple products as well as long-awaited support for PDF data integration in MapForce. Other features include Markdown editing support, split output preview for business report creation, support for new XBRL standards, and much more.
Let’s take a look at the highlights.
5 Reasons to Choose a Graphical JSON Schema Editor
The advantages of JSON as a lightweight, human-readable, interoperable data format have led to its widespread adoption in various domains, including web development, mobile app development, and backend services. Many programming libraries and frameworks provide built-in support for JSON parsing and serialization.
That said, most applications still benefit from or require validation of client-submitted data. Enter the JSON Schema spec, which lets you describe the structure of JSON data for a particular application, for both documentation and validation purposes.
Though JSON Schema code is by design human-readable, building a complex schema with nested and repeating sections in a text-only editor becomes time consuming and error-prone quickly. Let’s look at five ways a graphical editor is a must-have for JSON Schema development.
AI-based Database Image Classification with Altova MapForce
One of the most common examples of AI in our everyday lives is facial recognition. Facial recognition is the process of identifying or verifying a person’s identity based on their face. Facial recognition is used in many applications, such as unlocking our phones with FaceID, tagging our friends on social media platforms like Facebook, and checking in at airports or hotels with biometric scanners. Facial recognition can make our lives more convenient and secure, but it can also raise some privacy and ethical concerns. For instance, how can we ensure that our facial data is not misused or stolen by hackers or malicious actors? How can we prevent facial recognition from being used for surveillance or discrimination? How can we ensure that facial recognition is accurate and fair, and does not have any biases or errors?
The paragraph above was generated by ChatGPT in response to my request to describe the benefits and risks of artificial intelligence and include a real-life example. It’s interesting that ChatGPT chose FaceID as the example, since FaceID is simply one variation of image analysis and AI-powered image classification offers potential to automate many real-world tasks.
One common use-case is a product catalog, wherein a company manages product information provided by many different manufacturers. A product loaded into that database may have a name that does not necessarily include a precise description of the item. For instance, wellington is a boot, fedora is a hat, a mongoose is a bicycle, and a yellow watermelon shiny needlefish is a fishing lure. We can make use of AI-powered image classification using the Microsoft Azure Cognitive Services Computer Vision API to address this problem. The Computer Vision Service takes the image data or URL as its input and returns information about the content. One service generates image classification tags based on a training set of recognizable objects, living beings, scenery, and actions that the Azure AI has been trained on. These tags allow us to categorize products in the database accordingly and may even correspond to search terms a user might provide to find products in the catalog.
AI-based support request sentiment analysis using MapForce and GPT-4
Automated sentiment analysis of text, such as user reviews, has historically been a challenge. Because of the myriad intricacies of natural language, systems faced difficulties in analyzing context and nuances. This required an inordinate amount of manual work to overcome.
One of the many useful capabilities of modern AI systems that are based on large language models (LLMs) such as OpenAI’s GPT-4 is that they are very good at sentiment analysis of natural text inputs. We can use that capability to build a very efficient database solution in MapForce that, for example, goes through all the new incoming records in a support database and automatically determines whether a particular support request or other customer feedback is positive, negative, constitutes a bug report, or should be considered as a feature request.
How to Create a Chained Data Transformation
Data mapping plays a vital role in modern data-driven organizations, enabling efficient data management and integration. Altova MapForce is a powerful, graphical data mapping tool that supports endless data transformation scenarios, including one-to-one, one-to-many, many-to-one, and chained data conversion.
While there are applications for each of these approaches, chained data mapping is especially helpful for complex data processing tasks where multiple stages of data manipulation are required. Here’s a look at the benefits of a chained data conversion approach – and a video of how MapForce makes the process easy and straightforward.
How to Convert Data in MapForce [Video]
Altova MapForce offers a powerful, yet easy-to-use, approach to converting and transforming data. Whether you need to write XML to a database, convert JSON to EDI, or map Excel to multiple different data formats, MapForce has you covered.
From simple one-to-one conversions to complex ETL scenarios, the MapForce approach is to represent data structures as graphical components. To associate fields, drag and drop connecting lines. A comprehensive library of data filters and functions is available for transforming data before writing it to the target.
We have recently revamped our series of Introduction to MapForce videos. Each short how-to gives a demo of a common MapForce scenario.
Start at the beginning to learn how MapForce works:
And follow along to learn:
You can follow along with the examples in these how-to videos by downloading a free, 30-day trial of MapForce. Check back for new MapForce videos, which are added often.
New Tools for Large XML and JSON Documents
Code editors with a graphical option in addition to the traditional text view are great for developers working with XML or JSON. Graphical editors like Grid View in XMLSpy provide a visual representation of the JSON or XML structure, making it easier to understand, navigate, and edit complex data hierarchies.
XMLSpy introduced Grid View years ago and has been improving on it ever since. The most recent update includes the ability to split the editing pane horizontally or vertically, which is especially handy when working with large documents.
Let’s see how it works.
Creating ESEF Filings Just Got Easier
To meet current mandates, ESEF (European Single Electronic Format) reports must be filed in XBRL. To accelerate the process, regulated companies can take advantage of intelligent software tools to fill the reports with data. One such tool from Altova allows organizations to create valid ESEF XBRL directly in Excel, where finance pros are already comfortable working. This allows them to create reports for filing without having to learn the intricacies of XBRL syntax.
Unlike some other XBRL filing mandates, ESEF requires filing companies to create their own extension taxonomy, which defines the entity-specific data rules for their report. To help customers with this step of the filing process, the Altova ESEF XBRL add-in for Excel includes a visual extension taxonomy designer that makes it a complete start-to-finish ESEF reporting solution.
Let’s see how it works.
Drive VDA EDI Conversion and Transformation with MapForce
EDI (electronic data interchange) messages are used in a variety of industries for transmitting business information such as invoices, shipping notices, and purchase orders—which were traditionally recorded on paper—electronically. EDI transactions are structured according to standards that describe the format of each message. Adherence to a standard format enables the messages to be transmitted electronically between the computer systems of trading partners without human interaction.
Different industries take advantage of specialized EDI formats that are relevant to their business, and one such popular format is VDA EDI, which is used by the German automotive industry.
To work with VDA messages efficiently, companies often need to transform them to other formats, for instance, for storage in a backend database, or convert them to other EDI message formats for compatibility among systems. Altova MapForce makes this easy, with support for VDA as well as several other popular EDI standards. Let’s see how it works.
Split Mode in Grid View and More in v2023r2
The latest release of Altova software includes ongoing enhancements for graphical XML and JSON editing in XMLSpy Grid View, new tools for working with XPath/XQuery, support for integrating VDA EDI data in MapForce, and much more.
Here’s a quick overview of all the new functionality being introduced.
Building Apps with an Intelligent Database Wizard
Data-driven solutions like database and enterprise apps rely on connection to, and interaction with, backend databases. Backend relational databases, however, store data in tables that reflect complex data relationships. This provides numerous advantages for effective data management and data integrity but can make it difficult to access and work with the data stored therein in new ways. App developers need to have a comprehensive understanding of database design principles and the SQL query language just to get started.
In contrast, real world data relationships most often represent parent-child relationships or even deeper hierarchical structure. As such, working with hierarchical data where relationships can be visualized in a tree structure can be much simpler and more flexible, leading to faster development. This approach is also more accessible to developers without extensive SQL expertise.
To make building apps that connect to the backend relational databases that are ubiquitous in today’s enterprise easier, faster, and available to a wider range of developers, Altova MobileTogether takes an entirely unique approach. Its visual Database Wizard lets developers easily build a query that returns hierarchical data, work with that data in the app, and then easily save the data back in hierarchical form, letting MobileTogether take care of normalizing the data and writing it back to the corresponding linked tables. Let’s take a look at how it works.
Role-based Access Control in Enterprise Apps
Enterprise database apps are increasing in prevalence due to their advantages for enabling access to—and easy management of—the ever-growing amount of critical data business users need to work with on a day-to-day basis. Unlike other types of business productivity apps, database apps must include measures for managing different levels of user access to maintain the security and integrity of the enterprise data they expose.
This can include managing read-only and editing access rights or restrictions on access to certain types of data. While it is essential to ensure that only authorized personnel have access to confidential data, levels of permissions often vary throughout an organization. Apps built using Altova RecordsManager include comprehensive tools for managing role-based access to database data that can reflect these complicated relationships that exist within an organization.
Let’s take a look at how RecordsManager makes it easy for app administrators to manage complex role-based permissions with visual tools.
Scripting App Behavior
The low code approach to app creation has been very effective at decreasing the complexity and learning-curve associated with building custom apps in response to quickly changing business requirements. Low code tools do the heavy lifting for developers and system administrators, freeing them to focus on business needs and rules rather than writing complex code.
To be truly successful, any low code approach must still include the ability to configure sophisticated app behavior in response to user input. This can be a challenge to implement with visual tools.
Altova RecordsManager offers a low code approach to creating database apps, with a built-in scripting editor that is purely visual. This offers the best of both worlds: easy app creation and sophisticated functionality customized for data-centric applications. Let’s see how it works.
How to Build a Database-driven App
We’ve updated our demo series on building an app that connects to a backend database using MobileTogether. The low-code approach to app development in MobileTogether extends to database connectivity: the Database Wizard includes a visual SQL statement editor that makes it easier than ever to connect to and work with backend database data in your apps.
This how-to video series takes you through the process of developing a sample Books Catalog app with rich functionality for searching, adding new database records, working with images, and more.
New App Development Tools
Altova releases new versions of its app development framework multiple times a year to introduce new features added in response to customer requests and feedback, as well as to add support for newer OS and database versions as they become available.
The latest release of MobileTogether and RecordsManager introduces important new functionality for building low-code and no-code apps.
How to Get EBA XBRL from Excel
Like other regulatory agencies around the world, the European Banking Authority (EBA) has standardized on XBRL for the transmission of data submitted by filing entities. Benefits of using XBRL include increased accuracy and efficiency of supervisory practices and risk identification. The use of XBRL benefits filing organizations as well, because the now-standardized data can be easily validated and then used further for automated report generation and other common requirements.
However, the challenge lies in getting backend data into a valid XBRL format according to the EBA Taxonomy, especially since the employees recording the data are generally financial professionals and not familiar with XBRL syntax.
Let’s take a look at how EBA reporting tools can make it easier.
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Validating and Debugging Data Transformations
Software developers and other data professionals often need to transform data from one format to another. These transformations can be simple one-to-one conversions or may require more complex manipulation. For instance, relationships must be generated when importing flat CSV files into a database, or source data may need to be split for the target, as in full name vs. first, middle, last, and optional suffix. Validating data transformation is critical to prevent data loss or corruption.
In an earlier post on Web service data integration, we combined a string value for GMT time with a numeric offset in seconds to generate the local time for weather forecasts. We created a user function that performed all the steps required to complete this operation. MapForce includes a powerful interactive data mapping debugger that can easily trace and validate this transformation. Let’s take a look at how it works.
Benefits of an XML Schema Manager
Developers working with XML often need to deal with multiple DTDs and XSDs that define industry-standard vocabularies. Whether it’s DITA for technical writing, HL7 for healthcare data, CbCR for financial reporting or any number of examples, it becomes a challenge to manage the various schemas—and numerous versions thereof—on a day-to-day basis.
For flexibility and convenience, all Altova XML-enabled products include its XML Schema Manager. This provides a centralized utility that makes it easy to download and manage industry schemas for use across the product line. Let’s see how it works.
ESEF Tools
The ESEF acronym has been top of mind for finance professionals across the EU and UK since mid 2019, when an upcoming reporting mandate from the European Securities and Markets Authority (ESMA) was announced.
ESEF, which stands for European Single Electronic Format, is a digital financial reporting standard based on XBRL. As of early 2020, companies on EU regulated markets are mandated to prepare their annual reports in accordance with ESEF rules.
What exactly is ESEF compliance, and what does it take to meet reporting requirements? Let’s take a look at the basics and some tools that make it easy.
Dark Mode and Much More in Version 2023
Long-time XMLSpy and MapForce customers may remember the fun drawings and depictions from Altova’s early ads and logos. Those dark spy images have given way to brighter colors and imagery over the years – but at the same time, developers have gravitated towards dark mode in their applications. Now, dark mode is finally available for XMLSpy and MapForce!
This fun option is being released alongside important support for additional standards and databases, a new tool for managing schemas across the product line, and a brand new product for building ESEF XBRL reports in Excel.