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Average Ratings 1 Rating
Description
Save hours by developing and maintaining ETL/ELT pipelines using an easy-to use environment that allows for effortless debugging. Visualize changes before deployment to simplify development, accelerate testing, and reduce debugging. Work with Python and our simple interface to foster team collaboration and save valuable time during the most difficult development stages. Consolidate data in any format, from any source and any size with no hesitations. Ensure the most accurate data by utilizing error flagging and debugging in real-time within specific data processes as well as across pipelines. Use compute, storage and network bandwidth to auto-scale infrastructure as data volume and speed increase. Real-time data observability can help you identify and pinpoint problems. Zoom in and thoroughly troubleshoot your data pipelines.
Description
iceDQ, a DataOps platform that allows monitoring and testing, is a DataOps platform. iceDQ is an agile rules engine that automates ETL Testing, Data Migration Testing and Big Data Testing. It increases productivity and reduces project timelines for testing data warehouses and ETL projects. Identify data problems in your Data Warehouse, Big Data, and Data Migration Projects. The iceDQ platform can transform your ETL or Data Warehouse Testing landscape. It automates it from end to end, allowing the user to focus on analyzing the issues and fixing them. The first edition of iceDQ was designed to validate and test any volume of data with our in-memory engine. It can perform complex validation using SQL and Groovy. It is optimized for Data Warehouse Testing. It scales based upon the number of cores on a server and is 5X faster that the standard edition.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Cloudera
No
Jenkins
No
Pricing Details
Free
Credit based pricing system
Free Trial
Yes
Free Version
Yes
Pricing Details
$1000
Free Trial
Yes
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
Yes
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Datorios
Founded
2020
Country
United States
Website
datorios.com
Vendor Details
Company Name
iceDQ
Founded
2005
Country
United States
Website
icedq.com
Product Features
Data Extraction
Disparate Data Collection
No
Document Extraction
No
Email Address Extraction
No
IP Address Extraction
No
Image Extraction
No
Phone Number Extraction
No
Pricing Extraction
No
Web Data Extraction
No
Data Management
Customer Data
No
Data Analysis
No
Data Capture
No
Data Integration
Yes
Data Migration
Yes
Data Quality Control
No
Data Security
No
Information Governance
Yes
Master Data Management
No
Match & Merge
No
ETL
Data Analysis
No
Data Filtering
Yes
Data Quality Control
No
Job Scheduling
Yes
Match & Merge
Yes
Metadata Management
No
Non-Relational Transformations
Yes
Version Control
Yes
Integration
Dashboard
Yes
ETL - Extract / Transform / Load
Yes
Metadata Management
No
Multiple Data Sources
Yes
Web Services
No
Master Data Management
Data Governance
Yes
Data Masking
No
Data Source Integrations
Yes
Hierarchy Management
No
Match & Merge
No
Metadata Management
Yes
Multi-Domain
No
Process Management
Yes
Relationship Mapping
Yes
Visualization
No
Product Features
Automated Testing
Hierarchical View
No
Move & Copy
Yes
Parameterized Testing
Yes
Requirements-Based Testing
Yes
Security Testing
No
Supports Parallel Execution
Yes
Test Script Reviews
No
Unicode Compliance
No
Big Data
Collaboration
No
Data Blends
No
Data Cleansing
No
Data Mining
No
Data Visualization
No
Data Warehousing
No
High Volume Processing
No
No-Code Sandbox
No
Predictive Analytics
No
Templates
No
Data Quality
Address Validation
No
Data Deduplication
No
Data Discovery
No
Data Profililng
No
Master Data Management
No
Match & Merge
No
Metadata Management
No
Data Warehouse
Ad hoc Query
No
Analytics
No
Data Integration
No
Data Migration
No
Data Quality Control
No
ETL - Extract / Transfer / Load
No
In-Memory Processing
No
Match & Merge
No
ETL
Data Analysis
No
Data Filtering
No
Data Quality Control
No
Job Scheduling
No
Match & Merge
No
Metadata Management
No
Non-Relational Transformations
No
Version Control
No