Average Ratings 0 Ratings
Average Ratings 6 Ratings
Description
DataMatch Enterprise (DME) is Data Ladder's entity resolution and data matching platform. It identifies records that refer to the same person, business, or entity across disconnected systems, then links and consolidates them into a single accurate record. Core functions include data profiling, standardization, matching, deduplication, and merging, supporting use cases such as Customer 360, KYC, fraud detection, and master data management.
The platform is available through a no-code visual interface for business users and a REST API for developers, allowing the same matching engine to be embedded in applications, data pipelines, or AI agent workflows. Match results are rule-based and traceable, so users can see the specific logic behind each linked record rather than a single opaque score.
Recent additions include entity graphs for visualizing connected records, live search for real-time matching, and Docker as a deployment option in addition to cloud and on-premises environments.
Independent benchmarking across 15 studies shows DME identifying 5 to 12% more matches than comparable tools, with fewer false positives and accuracy up to 99%. In a large-scale test, it processed 10 million records in 41 minutes.
Description
Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
AWS Glue
No
Amazon S3
No
Amazon Web Services (AWS)
No
Apache Airflow
No
Azure Cosmos DB
No
Azure SQL Database
No
Cloudera
No
Databricks
No
Google Cloud BigQuery
No
Google Cloud Dataflow
No
Integrations
AWS Glue
Yes
Amazon S3
Yes
Amazon Web Services (AWS)
Yes
Apache Airflow
Yes
Azure Cosmos DB
Yes
Azure SQL Database
Yes
Cloudera
Yes
Databricks
Yes
Google Cloud BigQuery
Yes
Google Cloud Dataflow
Yes
Pricing Details
starts at $10000/user per year
Free Trial
Yes
Free Version
No
Pricing Details
Consumption-based and annual fixed licensing fee are both available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
Yes
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Data Ladder
Founded
2006
Country
United States
Website
dataladder.com
Vendor Details
Company Name
FirstEigen
Founded
2015
Country
United States
Website
firsteigen.com/databuck/
Product Features
Data Cleansing
Address/ZIP Code Cleaning
Yes
Charting
No
Data Consolidation / ETL
No
Data Mapping
Yes
Multi Data Format Support
Yes
Phone/Email Validation
Yes
Raw Data Ingestion
No
Sample Testing
No
Validation / Matching / Reconciliation
Yes
Data Quality
Address Validation
Yes
Data Deduplication
Yes
Data Discovery
No
Data Profililng
Yes
Master Data Management
No
Match & Merge
Yes
Metadata Management
No
Product Features
Big Data
Collaboration
No
Data Blends
No
Data Cleansing
No
Data Mining
No
Data Visualization
No
Data Warehousing
No
High Volume Processing
Yes
No-Code Sandbox
No
Predictive Analytics
No
Templates
No
Data Governance
Access Control
No
Data Discovery
No
Data Mapping
No
Data Profiling
No
Deletion Management
No
Email Management
No
Policy Management
No
Process Management
No
Roles Management
No
Storage Management
No
Data Management
Customer Data
No
Data Analysis
No
Data Capture
No
Data Integration
No
Data Migration
No
Data Quality Control
No
Data Security
No
Information Governance
No
Master Data Management
No
Match & Merge
No
Data Quality
Address Validation
No
Data Deduplication
No
Data Discovery
No
Data Profililng
Yes
Master Data Management
No
Match & Merge
No
Metadata Management
No
