Database Mapping Archives

Tag Archive for: Database Mapping

AI-based Database Image Classification with Altova MapForce

July 31, 2023/in Data Integration, Database mapping, Tools /by David McGahey

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.

decorative image depicting an AI "brain"

New Data Integration Tools

March 25, 2020/in Data Integration, Database, EDI /by Erin Cavanaugh

Altova MissionKit tools offer numerous ways to connect to, query, and integrate data from disparate sources. With multiple product releases each year, we’re constantly working to deliver increased power and efficiency for data integration, while adding features requested by customers. This includes ongoing updates to built-in support for all major SQL databases across the product line.

Let’s take a look at some of the recently added tools and enhancements.

New data integration tools in Altova's release

Data Mapping JSON Lines

February 24, 2020/in Data Integration, Database mapping, JSON /by David McGahey

The JSON data format continues to evolve as an open standard as it is creatively applied to new data interchange requirements. JSON Lines, defined at http://jsonlines.org/, is a convenient text format for storing structured data where each record is a single line and a valid JSON object. JSON Lines handles tabular data and clearly identifies data types without ambiguity. This allows records to be processed one at a time, which makes the format very useful for exporting and sending data.

Altova MapForce supports data mapping JSON Lines as either a data source or target. Let’s look at a mapping project to extract records from a database table and map to a JSON Lines file for output.

Transitioning Data Mapping Projects from Development through Testing and Production

November 29, 2019/in Data Integration, Database mapping, Servers, Software /by David McGahey

Data mapping projects often mirror software development efforts with distinct phases for design, testing, and deployment. This is especially true for ETL (Extract Transform Load) projects when repeated data mapping execution is required as new data becomes available, and the stakes increase higher with large data sets. The Altova MissionKit and Server Software products provide Global Resources to define configurations for each project phase and smoothly transition between them.

Let’s take a look at an example based on a MapForce data mapping from a source file to a database.

Database Mapping with Database Exception Handling

June 5, 2019/in Data Integration, Database, Database mapping /by David McGahey

Critical business processes depend on reliable data and database administrators and other data analysts want to be confident in the integrity of information stored in database tables. During automated ETL (Extract Transform Load) operations or other database import tasks, invalid data might be encountered that jeopardizes success of the procedure. Altova MapForce includes database exception handling to roll back the affected data when an error occurs and optionally proceed with the rest of a database mapping.

For instance, an error in a single record need not prevent execution of a mapping from continuing, such as when certain database constraints prevent the mapping from inserting or updating invalid data.

Database Tracing to Log Changes Made by a Data Mapping Project

May 1, 2019/in Data Integration, Database, Database mapping, Database tracing /by David McGahey

Database administrators and other data professionals often want to maintain a record of changes in critical databases, especially when updates are made by automated scripts or other operations. Database tracing lets administrators track critical changes or anomalies, and help recover from errors. Altova MapForce supports database tracing for all popular relational databases to log the changes made by a data mapping project to the database when the mapping runs.

When tracing is enabled, events such as database insert or update actions, or errors, are logged in an XML file that you can later analyze or process further in an automated way.

Database tracing can be enabled at the database component, table, stored procedure, or database field level. You can choose to trace all messages or only errors, or you can disable tracing completely.

In addition to tracing errors that occur during the execution of a mapping to a target database, MapForce also enables database transaction handling to roll back the affected part of the database data when an error occurs, then optionally proceed with the rest of the mapping.

MapForce Supports SQL Merge When It’s the Right Tool for the Job

November 1, 2016/in Data Integration, Tools /by David McGahey

Large database tables can easily contain a million, even hundreds of millions of rows of data. Database administrators and others charged with maintaining such large datasets are always concerned about execution time for ETL (Extract, Transform, and Load) operations, updates, and other SQL queries. To make these operations more efficient, some — but not all — database vendors implemented a SQL merge statement to insert or update rows of an existing table as a single bulk-insert statement rather than requiring individual statements for each row.

Altova MapForce automatically supports SQL merge when it is available for the target database. Let’s look at an example.

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