Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
DataOps ETL Validator stands out as an all-encompassing tool for automating data validation and ETL testing. It serves as an efficient ETL/ELT validation solution that streamlines the testing processes of data migration and data warehouse initiatives, featuring a user-friendly, low-code, no-code interface with component-based test creation and a convenient drag-and-drop functionality. The ETL process comprises extracting data from diverse sources, applying transformations to meet operational requirements, and subsequently loading the data into a designated database or data warehouse. Testing within the ETL framework requires thorough verification of the data's accuracy, integrity, and completeness as it transitions through the various stages of the ETL pipeline to ensure compliance with business rules and specifications. By employing automation tools for ETL testing, organizations can facilitate data comparison, validation, and transformation tests, which not only accelerates the testing process but also minimizes the need for manual intervention. The ETL Validator enhances this automated testing by offering user-friendly interfaces for the effortless creation of test cases, thereby allowing teams to focus more on strategy and analysis rather than technical intricacies. In doing so, it empowers organizations to achieve higher levels of data quality and operational efficiency.
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.
API Access
Has API
No
API Access
Has API
No
Integrations
Azure Databricks
Yes
Azure Synapse Analytics
Yes
Datagaps DataOps Suite
Yes
Microsoft Power BI
Yes
Oracle Analytics Cloud
Yes
Salesforce
Yes
Snowflake
Yes
Tableau
Yes
Integrations
Azure Databricks
No
Azure Synapse Analytics
No
Datagaps DataOps Suite
No
Microsoft Power BI
No
Oracle Analytics Cloud
No
Salesforce
No
Snowflake
No
Tableau
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
Free
Credit based pricing system
Free Trial
Yes
Free Version
Yes
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
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
No
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)
No
In Person
Yes
Vendor Details
Company Name
Datagaps
Country
United States
Website
www.datagaps.com/etl-validator/
Vendor Details
Company Name
Datorios
Founded
2020
Country
United States
Website
datorios.com
Product Features
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
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