Best Data Matching Software for PostgreSQL

DataBuck is an AI-powered data validation platform that automates risk detection across dynamic, high-volume, and evolving data environments. DataBuck empowers your teams to: ✅ Enhance trust in analytics and reports, ensuring they are built on accurate and reliable data. ✅ Reduce maintenance costs by minimizing manual intervention. ✅ Scale operations 10x faster compared to traditional tools, enabling seamless adaptability in ever-changing data ecosystems. By proactively addressing system risks and improving data accuracy, DataBuck ensures your decision-making is driven by dependable insights. Proudly recognized in Gartner’s 2024 Market Guide for #DataObservability, DataBuck goes beyond traditional observability practices with its AI/ML innovations to deliver autonomous Data Trustability—empowering you to lead with confidence in today’s data-driven world.

Senzing® entity resolution API software provides the most advanced, affordable, and easy-to-use data matching and relationship detection capabilities available. With Senzing software, you can automatically resolve records into common entities in real time as new data is received. The complete view of all records related to every person or organization, across all of your internal and external data sources, can help you reduce costs and enable new revenue opportunities. Companies use Senzing entity resolution API to provide highly accurate views of people, organizations, and their relationships. You can deploy the Senzing entity resolution API on premises or in cloud-native deployments. Data remains in your ecosystem and never flows to Senzing. A free proof of concept can be completed in one day on AWS or on BareMetal. Senzing makes human-intelligent decisions without any pre-training or pre-tuning.

Project is basic entity in QDeFuZZiner software. Each project contains definition of two source datasets to be imported and analyzed (so-called "left dataset" and "right dataset"), as well as variable number of corresponding solutions, which are stored definitions of how to perform fuzzy match analysis. On creation, each project is assigned unique project tag. During raw data importing to server, corresponding input tables get that tag appended in their name. This way, imported tables are always tagged by the project name, which ensures their uniqueness. During importing and also later on, during solutions creation and execution, QDeFuZZiner is creating various indexes on the underlying PostgreSQL database, which facilitate fuzzy data matching. Datasets are imported from source spreadsheet (.xlsx, .xls, .ods) or CSV (comma separated values) flat files to server database, where corresponding left and right database tables are then created, indexed and processed.