Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
DQV is the comprehensive data quality and testing platform developed by Kumaran Systems, catering to teams engaged in the movement, masking, or validation of substantial data volumes. This tool meticulously evaluates source and target datasets on a field-by-field basis, identifies discrepancies, and produces mismatch reports, eliminating the need for manual checks using spreadsheets.
The platform encompasses five key functionalities: it performs field-level comparisons while detecting drift, facilitates migration mapping between different schemas, offers deterministic PII masking, conducts record- and table-level validation with the ability for on-the-fly corrections, and generates synthetic data for teams lacking access to production data for testing.
DQV is compatible with a wide range of data sources, including SQL Server, Oracle, MySQL, PostgreSQL, AWS, Azure, GCP, flat files, JSON, XML, and REST APIs, and it seamlessly integrates with tools like Informatica, Databricks, and CI/CD pipelines, or can operate independently as a library or CLI tool.
In real-world applications, DQV has successfully validated an impressive 26.6 million bank records in less than 22 minutes, showcasing its efficiency and speed. Additionally, a free trial is offered, along with various licensing options, including individual, enterprise, and on-premises plans, ensuring flexibility for different organizational needs. This versatile solution not only enhances data integrity but also streamlines the testing process across multiple platforms.
Description
Examine the usage of your data assets, focusing on aspects like popularity, utilization, and schema coverage. Gain vital insights into your data assets, including their quality and usage metrics. You can easily locate and filter the necessary data by leveraging metadata tags and descriptions. Additionally, these insights will help you drive data governance and establish clear ownership within your organization. By implementing a streamlined lineage from data lakes to warehouses, you can enhance collaboration and accountability. An automatically generated field-level lineage map provides a comprehensive view of your entire data ecosystem. Moreover, anomaly detection systems adapt by learning from your data trends and seasonal variations, ensuring automatic backfilling with historical data. Thresholds driven by machine learning are specifically tailored for each data segment, relying on actual data rather than just metadata to ensure accuracy and relevance. This holistic approach empowers organizations to better manage their data landscape effectively.
API Access
Has API
No
API Access
Has API
No
Screenshots View All
No images available
Integrations
Amazon Kinesis
No
Amazon Redshift
No
Amazon S3
No
Apache Kafka
No
Azure Data Lake
No
Azure Synapse Analytics
No
Databricks
No
Gmail
No
Google Cloud BigQuery
No
Google Cloud Pub/Sub
No
Integrations
Amazon Kinesis
Yes
Amazon Redshift
Yes
Amazon S3
Yes
Apache Kafka
Yes
Azure Data Lake
Yes
Azure Synapse Analytics
Yes
Databricks
Yes
Gmail
Yes
Google Cloud BigQuery
Yes
Google Cloud Pub/Sub
Yes
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
No price information available.
Free Trial
No
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
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)
No
Online Support
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
No
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
Kumaran Systems
Founded
1992
Country
United States
Website
kumaran.com/products/dqv/
Vendor Details
Company Name
Validio
Founded
2019
Website
validio.io
Product Features
Data Quality
Address Validation
No
Data Deduplication
No
Data Discovery
No
Data Profililng
No
Master Data Management
No
Match & Merge
No
Metadata Management
No
Product Features
Data Lineage
Database Change Impact Analysis
No
Filter Lineage Links
No
Implicit Connection Discovery
No
Lineage Object Filtering
No
Object Lineage Tracing
No
Point-in-Time Visibility
No
User/Client/Target Connection Visibility
No
Visual & Text Lineage View
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