Organizations struggle to locate and understand the vast amounts of data spread across silos, making it difficult to gain a clear view of their data landscape.
Difficulty in Understanding Data Context
Without semantics-driven discovery, data assets often lack the necessary context, making it challenging for teams to interpret and categorize data accurately for their specific use cases.
Missing Lineage and Impact Analysis
The absence of visual lineage and automated impact analysis creates challenges in understanding the relationships between data assets, leading to potential downstream issues.
Fragmented Metadata and Context
Data often lacks proper classification, tagging, and context, leaving teams without the insights they need to confidently use or trust the data.
Overcome your Observability Challenges with DQLabs
Data Content Observability
Maintain Data Integrity with Near Real-Time Monitoring and Automated Insights
Continuous Data Quality Monitoring: Track data health to detect anomalies, missing values, and data integrity issues.
Schema Evolution Tracking: Automatically monitor schema changes to ensure compatibility and prevent unexpected downstream disruptions.
Rule-based & ML-driven Validation: Apply both rules and machine learning based DQ checks to enforce data standards and catch quality issues proactively.
Data Pipeline Observability
Full Visibility and Control Over Data Pipelines from End-to-End
End-to-End Data Lineage: Trace data flow across pipelines, making it easy to diagnose bottlenecks and pinpoint errors.
Real-Time Pipeline Monitoring: Get instant alerts on data delays, failures, or unexpected transformations within pipelines.
Impact Analysis: Assess how pipeline issues affect downstream systems, helping to prioritize fixes and minimize disruptions.
Performance Metrics: Monitor throughput, latency, and resource usage to optimize pipeline performance and reduce processing time.
Users, Usage, and Utilization
Track Data Access, Usage Patterns, and Utilization for Improved Governance and Efficiency
User Access Monitoring: Track user interactions with data to identify usage patterns and ensure compliance.
Data Usage Analytics: Understand how data assets are consumed across the organization to inform access and retention policies.
Utilization Insights: Gauge the effectiveness of data resources, identifying underused datasets or overutilized pipelines.
User Behavior Analysis: Detect unusual usage patterns that could indicate potential data misuse or compliance risks.
Proactive Alerting and Notifications
Stay Ahead of Data Issues with Custom Alerts and Instant Notifications
Customizable Alert Thresholds: Set threshold alerts for data anomalies, schema changes, and pipeline issues.
Real-Time Notifications: Receive instant alerts across channels (email, SMS, Slack) for immediate response.
Priority-Based Alerts: Classify alerts by impact level to focus on the most critical issues first.
“DQLabs helped us deliver a consistent source of high-quality data to address high-risk populations’ concerns and improve public safety and community planning.”
Tacoma Public Utilities Enhances Data Discovery with DQLabs
8x
Improvement in data discovery and issue resolution
“By using DQLabs, We automated the end-to-end business process and were able to continuously monitor the critical data elements with better productivity..”
Seamlessly integrate with your Modern Data Stack
Recognitions & Partnerships
Let our experts show you the combined power of Data Observability, Data Quality and Data Discovery.
