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features
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support

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Write a Review

Description

Kensu provides real-time monitoring of the complete data usage quality, empowering your team to proactively avert data-related issues. Grasping the significance of data application is more crucial than merely focusing on the data itself. With a unified and comprehensive perspective, you can evaluate data quality and lineage effectively. Obtain immediate insights regarding data utilization across various systems, projects, and applications. Instead of getting lost in the growing number of repositories, concentrate on overseeing the data flow. Facilitate the sharing of lineages, schemas, and quality details with catalogs, glossaries, and incident management frameworks. Instantly identify the underlying causes of intricate data problems to stop any potential "datastrophes" from spreading. Set up alerts for specific data events along with their context to stay informed. Gain clarity on how data has been gathered, replicated, and altered by different applications. Identify anomalies by analyzing historical data patterns. Utilize lineage and past data insights to trace back to the original cause, ensuring a comprehensive understanding of your data landscape. This proactive approach not only preserves data integrity but also enhances overall operational efficiency.

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

Screenshots View All

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 
Google Cloud Storage No 
Microsoft Teams No 
PagerDuty No 
PostgreSQL No 
SQL Server No 
Slack No 
Snowflake No 
dbt 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 
Google Cloud Storage Yes 
Microsoft Teams Yes 
PagerDuty Yes 
PostgreSQL Yes 
SQL Server Yes 
Slack Yes 
Snowflake Yes 
dbt 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) 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 Yes 

Types of Training

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

Vendor Details

Company Name

Kensu

Country

United States

Website

www.kensu.io/platform

Vendor Details

Company Name

Validio

Founded

2019

Website

validio.io

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 

Alternatives

Alternatives