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.

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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.

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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.

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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:

  1. Specifying a collection of files in the same directory using a wildcard
  2. 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.

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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.

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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.

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Extract Data for PDF Mapping


MapForce, Altova’s award-winning data mapping tool, now supports PDF input in data integration and ETL workflows. The powerful 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.

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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.

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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.

JSON Schema Editor in XMLSpy
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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

Introducing the latest MobileTogether release
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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.

EBA XBRL from Excel

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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.

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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.

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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.

Altova recently introduced a new XML Schema Manager integrated in its XML-enabled products. 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.

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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.

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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.

Altova announces Software Version 2023 with dark mode
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The Easy Way to Track GDPR Compliance


GDPR is an acronym that has been top of mind for of privacy officers, CIOs, and even marketers across the EU since the early months of 2018. Now that it’s been a few years since the regulation went into effect, organizations should have a handle on what type of data is impacted and how to handle compliance. The ongoing challenge is tracking and documenting the steps required for GDPR compliance as a business evolves over time.

While numerous one-off templates to create GDPR reports based on a moment in time are available, Altova has created the first long-term solution for documenting and tracking the entire GDPR compliance management process.

Let’s take a look at what GDPR compliance entails and how the Altova GDPR Compliance Database makes managing it organized and straightforward.

An easy way to document and track GDPR compliance
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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.

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How to Build a Data Entry App


Data entry is a vital activity for businesses and organizations across every vertical. While much data entry has been automated thanks to advances in technology, there are circumstances where manual input is still required. Whether entered data is for reporting financials, tracking research, documenting health data, or managing inventory, end users need easy-to-use tools that help them quickly enter valid information.

As apps have evolved, so has data entry software, offering users new options for getting the job done in the field, in the lab, or at a desk – on the user’s device of choice. App developers are challenged to quickly customize data entry apps with advanced features for automatic field population, validation checks, and reporting tools.

No-code app development frameworks offer a viable solution for building data entry apps for all platforms quickly and without a huge investment. Let’s take a look at some best practices and how no-code solutions can help tick the boxes.

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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.

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Getting Started with Altova RecordsManager


RecordsManager is a new tool from Altova to build business database solutions in record time using a powerful visual design interface. RecordsManager is a free, pre-built MobileTogether solution that is automatically available when you install MobileTogether Designer. The pre-built solution includes sample data sets, and the MobileTogether Simulator previews execution of the database solution right inside the free to use MobileTogether Designer. Getting started with Altova RecordsManager is just one click away when you launch the Designer. Soon you will be building your own custom database apps without needing backend development or manual coding.

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How to Build an Online Database – Without Coding


Backend databases are the lifeblood of enterprise and records-driven apps, but database development is time and resource intensive. Developers and administrators need easy tools for defining online databases to power the custom apps their businesses require to remain productive and competitive.

Altova RecordsManager offers an entirely visual approach to building sophisticated database apps without any coding or backend database development required. You can quickly define a simple or complex online database using an easy-to-use, entirely visual interface. Let’s see how it works.

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Certified Tools for xBRL-JSON & xBRL-CSV


XBRL International has finalized the sunrise period for its important new OIM (Object Information Model), which includes the xBRL-JSON and xBRL-CSV standards. In turn, it has completed the software certification process. Any product awarded the XBRL Certified Software designation has been thoroughly tested by XBRL International for conformance with the current XBRL specifications.

OIM represents a years-long effort of the XBRL community to modernize the financial reporting standard, providing a model for easily transforming XBRL data between XML and other popular formats like CSV and JSON. This way, organizations can take advantage of the functionality of XBRL and at the same time have XBRL documents written in the format(s) most convenient for them.

Altova XMLSpy and RaptorXML Server were some of the very first tools on the market to support xBRL-JSON and xBRL-CSV and are now officially named XBRL Certified Software for the OIM standards (in addition to being certified for a variety of other core XBRL technologies).

Learn more:

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Build No-Code Database Apps with RecordsManager


We are excited to announce availability of a new product in the Altova app development framework: RecordsManager.

Altova RecordsManager offers a completely visual, no-code interface for quickly creating custom database apps. RecordsManager is perfect for any app that handles data in records: think contract management, a customer database, an invoicing system, a database of local attractions or collections – the sky is the limit.

Your RecordsManager app will automatically be available on desktop devices as well as on mobile using native iOS and Android apps and provides tons of features that make it easy for end-users. Let’s see how it works.

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MobileTogether Gets a Major Update


Version 8.0 of MobileTogether adds several exciting new features to the innovative platform for building enterprise and mobile apps, giving existing customers a major upgrade and paving the way for new customers to create full-featured apps even faster than before.

Major additions to the platform include a brand new way of interacting with relational databases, support for modularization, and much more.

Version 8.0 also coincides with the launch of Altova RecordsManager, a new offering that gives system administrators a completely no-code option for creating business database apps in MobileTogether Designer.

Let’s take a look at the highlights.

Announcing MobileTogether 8.0
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Working with Avro Big Data in Your Favorite XML Editor


Big Data trends have developers working with XML alongside other data protocols such as JSON and Apache Avro, and XMLSpy supports both of these with dedicated editing views and functionality.

Let’s see how specialized Avro support in XMLSpy makes visualizing and searching Avro files, as well as editing Avro schemas, uniquely easy. We’ll also look at some of the advantages of utilizing RaptorXML Server for high-performance Avro processing.

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Automatically Convert EDI to XML


Electronic Data Interchange (EDI) has proven to be a durable business-to-business communication technology in use today with history dating to the 1960s and even earlier. Efficient EDI data encoding reduced transaction payload size and improved data transfer speeds at a time when messages were sent over teletype at speeds equivalent to a 300 baud modem. EDI standards bodies promote wide acceptance among enterprises, and systematic evolution of new EDI formats extends support across industries. EDI improves profitability and is a dominant format for e-commerce data exchange.

Despite the advantages, EDI files are barely human readable and need to be translated and mapped for compatibility with other business technologies. Altova MapForce is a graphical EDI mapping tool with native support for all major business data formats in use today, including XML, JSON, databases, flat files, Excel, and Web services, as well as the EDIFACT, X12, HL7, NCPDP SCRIPT, IDoc, PADIS, and SWIFT EDI transaction sets. MapForce can even automatically convert EDI to XML without the need to specify a target XML Schema or perform any manual mapping.

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How to Compare CSV Files or Compare a CSV File to a Database Table


CSV files are a quick and convenient way to record structured data in a generic format. Because CSV files are so easy to create, multiple similar versions of very large CSV files can quickly proliferate. Often it becomes necessary to compare CSV files to find the desired version. In an ETL scenario, a data analyst may want to compare a CSV file to a database table for validation or to update data.

DiffDog, the unique XML-aware diff / merge tool from Altova, supports CSV as a native file format for comparison and can compare and selectively merge data CSV to CSV, or between a CSV file and database table. Let’s look at an example.

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Analyze JSON Data with Filters, Formulas, and Charts


Software developers and other data professionals often need to examine new data instances before designing processes for efficient production. As JSON becomes a more popular format for data exchange, the tradeoff for smaller data payloads can be loss of clarity of the underlying data structure.

XMLSpy has supported viewing, modeling, and editing JSON files since 2010 and includes rich tools to analyze JSON data, including applying filters, formulas, and charts.

Let’s take a look.

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InfoPath Alternatives from Altova


InfoPath, the popular business forms software from Microsoft, was sunset by the company starting in 2016. Without a direct replacement, customers have turned to InfoPath alternatives to facilitate forms creation and automated data collection.

Altova offers two alternatives that meet different customer implementation requirements. This article will walk you through some background information and help you decide which product to choose.

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How to Debug XSLT and XQuery


Nothing’s more frustrating than getting unintended results from an XSLT or XQuery transformation and having to spend hours tracking down the issue – especially if you’ve inherited the project from another developer or haven’t looked at the code in a few months.  Of course, XMLSpy has long included an XSLT debugger and XQuery debugger for setting break points and stepping through transformations to identify problems.

For a more interactive debugging process, XMLSpy also includes XSLT/XQuery back-mapping.

With back-mapping enabled, you can simply click on or hover over the portion of your output document you want to zero in on, and XMLSpy will immediately highlight the source XML and XSLT or XQuery instruction that is responsible. Let’s see how it works.

Debug XSLT with back-mapping

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Mobile App Themes Improve User Satisfaction


Mobile app themes improve user satisfaction by letting each user customize the app appearance with personal preferences. One way to implement multiple themes in apps that report and visualize data is to offer users a choice of color palettes for charts and graphs. MobileTogether uses a combination of drag-and-drop UI design, the powerful Action Tree visual programming language for event handling, and standardized functional programming for data selection and processing. Developers use MobileTogether every day to create elegant cross-platform apps with rich charts and graphs in all popular formats.

The main chart configuration settings let mobile app developers choose any of four built-in color palettes or even define a custom color palette for charts and graphs as they design an app. Combined with dark mode and light mode display settings, that creates ten colorful possibilities for display customization. Rather than impose a design-time color choice, developers can leverage MobileTogether features to let users choose for themselves. Let’s look at an example.

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Transforming and Converting Protobuf


MapForce supports mapping protocol buffers (Protobuf) to and from 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 Protobuf using Java, Python, C++, C#, Ruby, and other programming languages.

The structure of any Protobuf 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 others.  .proto files versions 2 and 3 are supported.

A MapForce data mapping creates compatibility between existing XML, JSON, database or legacy data formats and new applications leveraging the efficiency of Protobuf.

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Excel Data Mapping to Update Existing Documents


Excel began life as a simple spreadsheet tool. Over time, support for rich text styling options, built-in charts, and copy and paste formatting features has led many enterprises to create reports in Excel documents. This can cause difficulty when data changes and existing documents need to be manually updated for distribution to a wide audience in the familiar report style.

Altova MapForce, the award-winning, graphical data mapping tool for any-to-any conversion and integration, supports Excel data mapping to convert data to existing Excel documents while preserving styles and formulas in the original.

This feature lets you write directly to nicely formatted Excel files to update data at runtime: any designated worksheets, rows, and cells from the specified file will be replaced with data from the mapping and all formatting in the existing file will be preserved as-is. To protect functionality in the existing spreadsheet, cells with formulas are not overwritten.

Let’s look at an example of how to map Excel data.

Financial pros using XBRL
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How to Compare XML and Other Files


The ability to diff and merge files is a necessity for every developer. This can be especially troublesome when trying to compare differences between files containing structured data, such as XML.

The video tutorial below provides an explanation on how to compare XML files – and more – using both XMLSpy and DiffDog. These powerful utilities perform diff and merge operations in an XML-aware manner, which reduces the number of false positives seen when comparing files.

Compare XML files with XMLSpy

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XML Reports to Text


Creating business reports from data stored in XML and relational databases is a common requirement in most organizations. However, it’s often complicated by the need to have information available in both web formats and print-ready formats such as PDF. Altova StyleVision takes a unique approach to report creation by letting users design reports from XML, databases, and even XBRL in a visual way – and output them in multiple formats simultaneously.

Over the years, support has been added for output to HTML, RTF, Word, and PDF – and now, based on customer feedback and requests, StyleVision also supports direct output to text. Let’s take a look at how it works.

Report designer
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Data Mapping NoSQL Databases


NoSQL databases are non-tabular databases that store data differently than traditional databases made up of relational tables. Two of the most popular NoSQL databases, MongoDB and Apache CouchDB, store data as collections of BSON (binary JSON) and JSON documents. These databases leverage flexible JSON schemas and scale easily with large amounts of data and high user loads.

Altova MapForce has long supported data mapping all popular relational databases and now also includes native support for data mapping NoSQL databases. MapForce includes functionality for inserting, extracting, filtering, and ordering NoSQL data. Let’s look at an example.

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