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features
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support

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Description

Eliminate data outages by proactively identifying and resolving data quality problems before they enter production. Achieve full test coverage of your data pipelines in just one day, going from 0 to 100%. With automatic regression testing across billions of rows, understand the impact of each code modification. Streamline change management processes, enhance data literacy, ensure compliance, and minimize the time taken to respond to incidents. Stay ahead of potential data issues by utilizing automated anomaly detection, ensuring you're always informed. Datafold’s flexible machine learning model adjusts to seasonal variations and trends in your data, allowing for the creation of dynamic thresholds. Save significant time spent analyzing data by utilizing the Data Catalog, which simplifies the process of locating relevant datasets and fields while providing easy exploration of distributions through an intuitive user interface. Enjoy features like interactive full-text search, data profiling, and a centralized repository for metadata, all designed to enhance your data management experience. By leveraging these tools, you can transform your data processes and improve overall efficiency.

Description

The JCov open-source initiative is designed to collect quality metrics related to the development of test suites. By making JCov accessible, the project aims to enhance the verification of regression test executions within OpenJDK development. The primary goal of JCov is to ensure transparency regarding test coverage metrics. Promoting a standard coverage tool like JCov benefits OpenJDK developers by providing a code coverage solution that evolves in harmony with advancements in the Java language and VM. JCov is entirely implemented in Java and serves as a tool to assess and analyze dynamic code coverage for Java applications. It offers features that measure method, linear block, and branch coverage, while also identifying execution paths that remain uncovered. Additionally, JCov can annotate the program's source code with coverage data. From a testing standpoint, JCov is particularly valuable for identifying execution paths and understanding how different pieces of code are exercised during testing. This detailed insight helps developers enhance their testing strategies and improve overall code quality.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AWS Marketplace
Amazon Corretto
Amazon Redshift
Amundsen
Apache Airflow
Apache NetBeans
Azure Databricks
Azure Marketplace
DataHub
GitHub
Google Cloud BigQuery
Helidon
PagerDuty
PostgreSQL
Red Hat Runtimes
Secoda
Slack
Snowflake
XML
dbt

Integrations

AWS Marketplace
Amazon Corretto
Amazon Redshift
Amundsen
Apache Airflow
Apache NetBeans
Azure Databricks
Azure Marketplace
DataHub
GitHub
Google Cloud BigQuery
Helidon
PagerDuty
PostgreSQL
Red Hat Runtimes
Secoda
Slack
Snowflake
XML
dbt

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
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

Datafold

Country

United States

Website

www.datafold.com

Vendor Details

Company Name

OpenJDK

Country

United States

Website

wiki.openjdk.org/display/CodeTools/jcov

Product Features

Data Quality

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

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