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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

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

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

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Azure Databricks
Azure Synapse Analytics
Cloudera
Datagaps DataOps Suite
Jenkins
Microsoft Power BI
Oracle Analytics Cloud
Salesforce
Snowflake
Tableau

Integrations

Azure Databricks
Azure Synapse Analytics
Cloudera
Datagaps DataOps Suite
Jenkins
Microsoft Power BI
Oracle Analytics Cloud
Salesforce
Snowflake
Tableau

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

$1000
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Datagaps

Country

United States

Website

www.datagaps.com/etl-validator/

Vendor Details

Company Name

Torana

Founded

2008

Country

United States

Website

icedq.com

Product Features

ETL

Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control

Product Features

Automated Testing

Hierarchical View
Move & Copy
Parameterized Testing
Requirements-Based Testing
Security Testing
Supports Parallel Execution
Test Script Reviews
Unicode Compliance

Big Data

Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates

Data Quality

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

Data Warehouse

Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge

ETL

Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control

Alternatives

Alternatives

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