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Description

Companies are increasingly transitioning their communication and collaboration platforms to cloud-based systems. To safeguard against potential vendor lock-in and interruptions, it is essential for them to have an independent backup or archival solution on a different cloud. Manual data operations are often vulnerable to risks such as data loss or theft. Therefore, it is crucial for emerging tools to accommodate a variety of data types and incorporate robust in-built checks for data integrity, facilitating swift and error-free migration of substantial data volumes. Legacy software typically necessitates dedicated physical hardware and lacks the capability to manage the data produced by modern SaaS applications. Additionally, these traditional tools demand significant human effort for data transformation. As more businesses adopt cloud communication services, the volume of generated data continues to increase. To effectively manage this data, organizations require automated systems that can scale efficiently. Consequently, there is a pressing need for hands-free automation solutions, ensuring that the data being processed remains secure and accessible. Furthermore, enhancing data security and operational efficiency will become increasingly vital as the reliance on cloud infrastructures grows.

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 

Screenshots View All

Screenshots View All

Integrations

Amazon S3 Yes 
Cloudera No 
Google Workspace Yes 
Jenkins No 
Microsoft 365 Yes 
Microsoft Exchange Yes 
Vaultastic Yes 
Zimbra Yes 

Integrations

Amazon S3 No 
Cloudera Yes 
Google Workspace No 
Jenkins Yes 
Microsoft 365 No 
Microsoft Exchange No 
Vaultastic No 
Zimbra No 

Pricing Details

$0.945 per GB
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) Yes 
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) Yes 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

LegacyFlo

Country

India

Website

legacyflo.mithi.com

Vendor Details

Company Name

iceDQ

Founded

2005

Country

United States

Website

icedq.com

Product Features

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 

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