iceDQ Features

Trusted by industry, secure & scalable

iceDQ is the only integrated platform for data testing, monitoring, and observability, offering vast capabilities to meet the most complex data reliability requirements.

Features

Key Features

Functionalities Data Testing Data Monitoring Data Observability
Rules and AI: Automatically generate rules and metrics.
Low Code No Code: Leverage a library of pre-built out-of-box templates and checks to set up your test cases quickly and efficiently.
Exception Report: Get granular data exceptions at record and column level.
Reporting Dashboard: Visualize prebuilt DQ dashboards.
DevOps Integration: Automate data quality checks within your CI/CD pipeline for continuous monitoring.
Test Case Management Integration: Connect with TCM tools to automate data validation.
Multi-Source data comparison: Compare and validate data sets from different sources.
Performance and Scalability: Scales efficiently to accommodate growing data volumes without compromising performance.
Multi-Tenancy: Efficiently manage and isolate data for multiple tenants within a single deployment, ensuring security and resource optimization.
API First: Design and build your integrations with a robust API-first approach, ensuring seamless connectivity across systems.
150+ Connectors: Connect to any databases, files, applications, systems, data lakes, big data platforms, and more.
Anomaly Detection: Utilize both machine learning and rule-based methods for comprehensive anomaly detection.

Security

Functionalities Data Testing Data Monitoring Data Observability
Supported Data Source Authentications: User Authentication, Windows Authentication, Active Directory, Kerberos Keytab, Kerberos Ticket Cache, OAuth 2.0, AWS IAM, Azure Key Vault, and more.
Single Sign-On: Simplify logins and access with Single Sign-On (SSO) integration.
Integrated Key Vault: Ensures secure access and management of encryption keys for heightened data protection.
LDAP / Active Directory: Integrate seamlessly with LDAP and Active Directory for centralized user management and authentication.
End-to-End Encryption: Data is fully encrypted in transit and at rest, ensuring complete security and privacy.

Rule Types

Functionalities Data Testing Data Monitoring Data Observability
Checksum Rule: Checksum rule is used to compare row count, sum, average, min/ max, and other aggregated values.
Reconciliation Rule: Recon rule compares data between the source and target. Database vs database, database vs files, API vs database, BI reports vs database, and more.
Validation Rule: Validation rule allows users to test the formats, lengths, ranges, business logic, and other aspects of data in a specific dataset.
Pushdown Rule: Use this rule to offload data validation processing to the source database or system. This rule is typically used for duplicate checks and type II dimension tests.
Script Rule: Write custom groovy script to execute any pre-processing or post-processing tasks and achieve end-to-end testing.
Regression Suite: Use our workflows feature to run a collection of rules in a sequence for Regression Testing or Release Testing.
Macro Metrics: Focus on aggregated data such as freshness and volume metrics.
Micro Metrics: Focus on individual records in the data such as completeness and accuracy.
Segmented Metrics: Segmented metrics are simply macro metrics applied to specific data subgroups, like volume by country code.
Autometrics: The system offers built-in data profiling and automatic metric generation. This enables rapid scaling of observability.

Connectivity

Functionalities Data Testing Data Monitoring Data Observability
Databases: Support for all kinds of databases such as DB2, MySQL, Oracle, PostgreSQL and more.
Data Lakes: Support for Amazon S3, Azure Blob Storage, Azure Data Lake Storage, Google Cloud BigLake and more.
Files: Connect, test, and write queries on various file types such as Excel, flat files, JSON, XML, Parquet, and more.
Cloud: Seamlessly integrate with popular cloud data platforms like Snowflake, Redshift, BigQuery, and others.
Big Data: iceDQ scales to conquer big data challenges, seamlessly integrating with Hadoop, Spark, and other big data frameworks.
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Integrations

Functionalities Data Testing Data Monitoring Data Observability
ALM: Seamlessly integrate with ALM tools like HP ALM and X-Ray for defect tracking and resolution.
Ticketing System: Easily integrate with ticketing systems like Jira and ServiceNow.
Scheduler: Out-of-Box integrations available for schedulers like Airflow, Autosys, and Control-M.
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Others

Functionalities Data Testing Data Monitoring Data Observability
Parameterization: Leverage parameters to define reusable rules that adapt to different environments and data sets.
User-Defined Functions: Extend iceDQ's capabilities by creating custom UDFs (User-Defined Functions) within the platform.
Rules Wizard: The Rules Wizard helps you create bulk rules in a flash.
Built-in Scheduler: Trigger rules and workflows based on a time-based schedule.
Alerts & Notifications: Receive timely alerts and notifications about data issues via email, Teams, or Slack.
User Role-based Controls: Assign roles with granular permissions to specific users.
Group-based Controls: Streamline user access control by creating groups and assigning roles at the group level.
External-Library Support: Users can import Java or custom libraries for use in advanced scripting rules.
Custom Tags & Label Support: Add custom labels and tags to rules, workflows, and checks for improved reporting, and easier identification of rule types.
Central Repository: Simplified management with a central repository for rules, setting and other critical configurations.
Audit History: Track every action with a detailed audit history.
Import-Export: Import or export rules, workflows and other configuration between environments.
Architecture: Based on microservices and follows an API-first approach.
Deployment: SaaS, On-premises, Cloud, more…
Query Builder: The intuitive query builder eliminates the need to write complex queries.