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Platform
- DQLabs Platform
One unified platform for data leaders, engineers, and analysts—offering observability, quality, discovery, and remediation.
- DQLabs Capabilities
Automated data quality, anomaly detection, semantics-driven discovery, and issue resolution for trusted and reliable data.
- Data Observability
Monitor data, pipelines, and usage—detect anomalies, ensure reliability, and optimize the health of your data systems.
- Data Quality
Automate data quality with no-code checks, anomaly detection, lineage, and governance for trusted, accurate, and reliable data.
- Data Discovery
Discover data with semantics-driven categorization, advanced search, and catalog integration for faster, actionable insights.
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End-to-End Data Observability for Reliable Data

Ensure data reliability with proactive monitoring, automated anomaly detection, and comprehensive visibility across data, pipelines, and reports.

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Common Data Observability Challenges

Ensuring Data Reliability

Unchecked data anomalies, schema changes, and duplicates undermine data reliability, leading to flawed insights and delayed decision-making.

Monitoring Complex Data Pipelines

Limited visibility into data pipelines makes it difficult to detect failures and inefficiencies, resulting in prolonged downtimes and disrupted data flows.

Managing Query Performance and Costs

Without clear insights into query execution, inefficient and long-running queries slow down performance, delay critical insights, and drive up compute costs.

Reducing Alert Fatigue

Excessive and poorly prioritized alerts, including false positives, can overwhelm teams, leading to critical issues being missed or ignored.

Data Content Observability

Out-of-the-box checks for Schema, Volume, Freshness, and Uniqueness to ensure data reliability.
AI/ML-driven anomaly detection and resolution of unknown anomalies.
Automated alert prioritization for effective issue resolution and reduced alert fatigue.

Pipeline Observability

Granular and component-level monitoring of pipelines (Runs, Jobs, Tasks, Tests) with component-specific charts and alerts linked to them.
Automated lineage to quickly trace and fix broken pipelines by identifying root causes of data issues.
Comprehensive pipeline management with support for dbt, Azure ADF, Airflow, Fivetran, and Talend.

Usage Observability

Centralized visibility into query behavior across all configured cloud data warehouses and RDBMS connections.
Granular query insights with detailed execution statistics (total queries, average, max, and min execution times) for each asset.
Enhanced cost management and query optimization by identifying and addressing long-running or inefficient queries, enabling proactive compute cost control.

Reports Observability

Centralized visibility into Power BI and Tableau reports to track data health and ensure report reliability.
Table and column-level lineage to trace upstream data issues, accelerating root cause identification and resolution.
Integration with collaboration tools like Slack or Jira for efficient communication and issue resolution across teams working on BI reports.

DQLabs ranks as a Leader in the Everest PEAK Matrix® Assessment for Data Observability.

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Frequently Asked Questions

Do you monitor data on-premises?

Yes, DQLabs is an ideal tool for monitoring and observability for multi-cloud or hybrid-cloud workloads. We support both on-premises and cloud environments.
How does DQLabs help with alert fatigue?

DQLabs’ AI/ML-driven anomaly detection categorizes all data quality anomalies as - high, medium, or low priority, based on the deviation from the historical data. This helps users to focus on high-priority alerts for an effective issue-resolution process.

How does DQLabs' data observability platform integrate with existing data tools and workflows?

DQLabs provides out-of-the-box connectors for major data warehouses, data lakes & lakehouses, ETL/ELT tools, catalogs and BI tools. Users can embed data quality checks in their data pipelines with our integrations with tools like Airflow and dbt. Our bi-directional integration with leading data catalogs like Collibra and Alation allows users to easily access data quality metrics of their datasets. To read more about our connectivity with other tools please visit our integration page.
How does DQLabs go beyond data observability?

DQLabs provides out-of-the-box table-level observability checks to detect volume, schema, freshness, and uniqueness related issues. DQLabs also provides attribute-level data observability with its AI/ML-driven anomaly detection. AI-powered anomaly detection automatically understands the trends and patterns of data and uses these benchmarks to generate alerts for data anomalies. DQLabs also provides a comprehensive lineage to show the complete data journey from upstream to downstream data sources which helps users in root cause analysis.