Zingg
Click to visit website
About
Zingg is an automated entity resolution platform designed to help organizations master their data by identifying and linking records that refer to the same real-world entity. By utilizing machine learning and fuzzy matching, the tool addresses the challenge of messy data—such as misspelled names, varying address formats, or incomplete contact information—across disparate sources. Its primary purpose is to create a continuously updated identity graph, enabling a true Customer 360 view without the need to move sensitive information out of the existing corporate environment. The tool operates natively on a company's existing data stack, supporting modern platforms like Microsoft Fabric. This zero-copy approach means data remains in the warehouse or lakehouse, ensuring high security and compliance while reducing processing latency. Key features include scalable identity resolution, the ability to handle massive datasets, and a machine learning core that learns from small sets of labeled data to improve matching accuracy over time. It effectively bridges the gap between fragmented raw data and a reliable source of truth for downstream business applications. Zingg is primarily built for data leaders, data engineers, and architects who need to maintain high-quality data for analytics, marketing, or operational purposes. It is particularly valuable for industries like retail, finance, and healthcare where understanding a single customer or entity across multiple touchpoints is critical for success. Unlike traditional rule-based matching systems that require extensive manual configuration and struggle with edge cases, Zingg uses AI to automate the discovery of links, making it more robust against variations in data quality. What distinguishes Zingg from many proprietary SaaS alternatives is its commitment to data ownership and its open-source foundation. The Zingg Community edition allows developers to get started via GitHub, benefiting from a transparent codebase and an active community of over 850 Slack members. This allows organizations to build a composable Customer Data Platform or Master Data Management solution that they fully control, avoiding the vendor lock-in and high costs often associated with enterprise identity resolution suites.
Pros & Cons
Open-source Community edition available on GitHub with over 1000 stars.
Supports zero-copy architecture to maintain data ownership and security.
Uses machine learning to resolve entities in messy and inconsistent datasets.
Natively integrates with modern data infrastructure like Microsoft Fabric.
Active community support with over 850 members on Slack.
Pricing for the Enterprise version is not transparently listed on the website.
Requires a modern data stack and technical expertise to deploy and manage.
Documentation is hosted externally which may require separate navigation.
Use Cases
Data engineers can implement automated entity resolution pipelines within their existing warehouse to create a unified view of customer records.
Marketing teams can build a composable Customer Data Platform to achieve a Customer 360 view for more personalized campaigns.
Data architects in regulated industries can perform identity resolution without moving sensitive data to external SaaS environments.
Analytics teams can improve the accuracy of their reports by deduplicating and linking fragmented data from multiple source systems.
Platform
Task
Features
• open-source core
• continuous identity updates
• microsoft fabric integration
• machine learning record linkage
• native data stack deployment
• zero-copy data processing
• identity graph creation
• ai-powered fuzzy matching
FAQs
What is the main benefit of Zingg's zero-copy architecture?
It allows identity resolution to happen natively on your existing data stack, such as Microsoft Fabric, without moving data to an external server. This ensures complete data ownership and maintains high security and compliance standards.
How does Zingg handle messy or inconsistent data?
Zingg uses AI-powered fuzzy matching and machine learning to identify records that refer to the same entity despite typos or different formats. It learns from labeled data to improve accuracy and handle complex matching scenarios that rules-based systems often miss.
Is there an open-source version of Zingg available?
Yes, the Zingg Community edition is available as an open-source project on GitHub. It has a growing community of over 1,000 stars and 850 Slack members for collaborative development and support.
Which data platforms does Zingg integrate with?
Zingg is designed to deploy natively on modern data stacks and warehouse environments. The documentation highlights specific integrations with Microsoft Fabric and the ability to work within existing data infrastructure pipelines.
Pricing Plans
Enterprise
Unknown Price• Identity resolution
• Native deployment
• Zero-copy architecture
• Enterprise support
• Identity graph maintenance
• Scalable data processing
Community
Free Plan• Open-source core
• Fuzzy matching
• Community support via Slack
• GitHub access
• Self-hosted deployment
Job Opportunities
Backend Engineer (Java)
Build a unified identity graph and resolve duplicate records across your data stack using AI-powered fuzzy matching for complete data ownership and accuracy.
Education Requirements:
Preferred degree in Computer Science and/or Maths
Experience Requirements:
0-3 years of professional software development experience as a Back-end Java Engineer
Practical experience writing reasonably complex software in Java 8+/Scala
Demonstrated experience in solving complex product development problems
Strong foundation and previous development experience in data structures, software design, and distributed and cloud architecture
Experience with Agile development, continuous integration, and DevOps culture
Other Requirements:
Open source experience
Working with Linux for code development
Experience in Docker
Exposure to Apache Spark and Snowflake APIs
Strong analytical and problem-solving skills
Responsibilities:
Driving new features for Zingg open source and enterprise products
Design classes/modules and write clean, maintainable, robust, and scalable code
Troubleshoot, debug, and fix Zingg issues
Address user questions and feedback
Rapidly prototype and adjust in response to customer feedback
Show more details
Ratings & Reviews
No ratings available yet. Be the first to rate this tool!
Alternatives
Fuzzy Match
Clean and resolve inconsistent datasets by identifying records with typos or misspellings using advanced machine learning and semantic analysis techniques.
View DetailsFeatured Tools
adly.news
Connect with engaged niche audiences or monetize your subscriber base through an automated marketplace featuring verified metrics and secure Stripe payments.
View Details
EveryDev.ai
Accelerate your development workflow by discovering cutting-edge AI tools, staying updated on industry news, and joining a community of builders shipping with AI.
View Details
Nana Banana Pro
Maintain perfect character consistency across diverse scenes and styles with advanced AI-powered image editing for creators, marketers, and storytellers.
View Details
Kling 4.0
Transform text and images into cinematic 1080p videos with multi-shot storytelling, character consistency, and native lip-synced audio for professional creators.
View Details
AI Seedance
Generate 15-second cinematic 2K videos with physics-based audio and multi-shot narratives from text or images. Ideal for creators and marketing teams.
View Details
Mistrezz.AI
Engage in immersive NSFW roleplay and ASMR voice sessions with adaptive AI companions designed for structured escalation, fantasy scenarios, and personal connection.
View Details
Seedance 3.0
Transform text prompts or static images into professional 1080p cinematic videos. Perfect for creators and marketers seeking high-quality, physics-aware AI motion.
View Details
Seedance 3.0
Transform text descriptions into cinematic 4K videos instantly with ByteDance's advanced AI, offering professional-grade visuals for creators and marketing teams.
View Details
Seedance 2.0
Generate broadcast-quality 4K videos from simple text prompts with precise text rendering, high-fidelity visuals, and batch processing for content creators.
View Details
BeatViz
Create professional, rhythm-synced music videos instantly with AI-powered visual generation, ideal for independent artists, social media creators, and marketers.
View Details
Seedance 2.0
Generate cinematic 1080p videos from text or images using advanced motion synthesis and multi-shot storytelling for marketing, social media, and creators.
View Details
Seedream 5.0
Transform text descriptions into high-resolution 4K visuals and edit photos using advanced AI models designed for digital artists and e-commerce businesses.
View Details
Seedream 5.0
Generate professional 4K AI images and edit visuals using natural language commands with high-speed processing for marketers, artists, and e-commerce brands.
View Details
Kaomojiya
Enhance digital messages with thousands of unique Japanese kaomoji across 491 categories, featuring one-click copying and AI-powered custom generation.
View Details