Overview
Spotter DQV accelerates data quality monitoring with integrated, customizable, cloud-ready open-source components that scale to the needs of your data platform and use cases.
- Incorporates open-source technologies like Kafka, Spark, HDFS, Yarn, Kibana and Elastic Search
- Supports both real-time streaming data and batch data
- Ensures low latency
- Integrates with metadata and metrics repositories
- Includes data quality monitoring dashboard
Level up trust in data to drive agility, productivity, cost effectiveness and compliance.
See how Spotter DQV improves all data-driven processes
Spotter DQV Benefits
Spotter DQV ensures compliant, high-quality enterprise data to help optimize AI models, analytics processes and more.
This data quality validation framework provides a unified view to enable the organization to monitor data quality and intervene if necessary, improving trust in and engagement with data for more effective data-driven operations.
Get greater value form data
Improve data quality by as much as 85% to improve trust in and engagement with data across all functions.
Ensure complete visibility
Provide a unified view of data quality across various dimensions and functional domains, saving 25-75% of monitoring time and effort.
Save on deployment and maintenance
Integrate seamlessly with your existing platform and applications to ensure uninterrupted operations and reduce repair turnaround times by ~70%.
Ensure security and compliance
Leverage built-in failure recovery mechanisms to enable resuming from points of process failure and ensure no data is missed during quality checks.
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Spotter DQV Components
The Spotter DQV framework houses a full range of components designed to promote easy and seamless integration into your existing environment and quickly unlock its full benefits.
Source connectors
Kafka and HDFS available in the staging area to enable connecting to both streamed and file-based source data
Metadata repository
Built-in central store of source details, quality check types and attributes and thresholds, as well as a consolidated input for the framework.
Metris Repository
Central metrics store with attribute and total view, as well as support for search and visualizations.
ML/DL models
Machine and deep learning models for outlier detection, data imputation and variance anomaly detection.
Monitoring dashboard
Visualizations of data quality check results, including overall summary and anomaly trend analysis across attributes and dimensions.
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