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Stay ahead of data issues by quickly detecting schema changes, duplicates, and null values.
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Data observability isn't just about tracking failures; it's about gaining a holistic view of your entire data ecosystem.
With our platform, you can monitor data flow from ingestion to consumption, ensuring every piece of data is accurate, timely, and relevant.
Data downtime can be costly. Our real-time alerting system ensures you’re immediately notified of any issues, allowing for quick intervention. Customize notifications to get the right alerts to the right people, keeping your data pipeline running smoothly.
Adopting a new tool shouldn't mean overhauling your existing systems. Decube integrates seamlessly with your current data stack, ensuring that you can start monitoring your data immediately without disrupting your workflows.
Setting up monitoring shouldn’t be a complex task. With our centralized control panel, you can easily configure and manage all your data monitoring needs from a single, intuitive interface. Streamline your monitoring setup process, reduce manual effort, and ensure consistency across your data assets, all in one place.
Flexibility is key when it comes to monitoring unique business scenarios. With Decube, you can create custom SQL monitors tailored to your specific use cases.
Whether you're tracking query performance or detecting anomalies, our solution allows you to closely monitor and address potential issues, ensuring your data operations align perfectly with your business objectives.
Optimize your data quality checks with a scheduling setup that fits your workflow. Decube allows you to configure and run data quality tests at intervals that suit your needs—whether daily, weekly, or on a custom schedule. Gain the flexibility to ensure your data is always reliable without disrupting your operations.
When you need to address data quality concerns quickly, Decube's on-demand monitoring empowers you to run tests and perform manual checks instantly. Whether you suspect an issue or need to verify data integrity, you can take immediate action to ensure your data remains accurate and trustworthy.
Fine-tune your alerting system to better suit your needs by providing feedback on ML-generated tests. With Decube, you can easily adjust the sensitivity of alerts, ensuring that you’re notified only when it truly matters. Train the system over time to reduce false positives and enhance its accuracy for your unique data environment.
Easily run tests and perform manual checks instantly if you suspect any data quality issues.
Detect issues in freshness, volume, schema, and quality before they reach your dashboards.
Learn normal patterns for freshness and volume and flag anomalies automatically, so you catch issues before they reach dashboards.
Track the health signals that matter across every table and pipeline, so nothing drifts unnoticed.
Validate accuracy, completeness, validity, and uniqueness with built in and custom rules.
Write your own checks for the quality rules only your team knows.
Send the right alert to the right team in Slack, email, or PagerDuty, with severity levels so noise stays low.
Triage incidents and see which dashboards and models an issue affects before it spreads.
Decube connects to your warehouses, lakes, and BI tools in minutes, with no data leaving your environment.
Decube learns normal behavior and watches freshness, volume, schema, and quality automatically.
Get routed alerts with root cause and downstream impact, and resolve incidents fast.
Same objective, one platform: catch broken pipelines and bad data before they reach your dashboards.
Continuously monitor freshness, volume, schema, and pipelines, and get alerted the moment something breaks.
Validate accuracy, completeness, and validity with ML monitors and custom SQL checks.
Decube unifies both, so you know the moment data breaks and whether the data you ship is trustworthy.
and many more...
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Decube runs on a metadata-only architecture and meets the standards regulated teams require.
Safeguarding your information with industry-leading standards.
Ensuring your information is protected with the highest level of integrity.
Ensuring the confidentiality and integrity of your healthcare data.
Protecting personal data with robust privacy and security measures.
Your data is encrypted in motion with TLS and at rest with AES-256.
If any of these sound familiar, your data needs monitoring you can trust.
You hear about broken data from stakeholders, not before them.
Dashboards silently show stale or wrong numbers.
Engineers spend hours firefighting pipeline failures.
No one knows the downstream impact of a failed job.
Data incidents have no owner or clear resolution path.
You cannot tell if today's data is fresh, complete, and correct.
Built for teams that run on trusted data, in industries where a broken pipeline turns into a business problem.
Catch broken data before it reaches risk, finance, and regulatory reports.
Freshness and volume monitoring on critical tables
ML anomaly detection on risk and finance metrics
Downstream impact analysis before issues spread
Data quality evidence ready for audit
Keep policyholder, claims, and transaction data trustworthy from source to report.
Continuous transaction quality monitoring
Schema drift detection across claims pipelines
Routed alerts with root cause and a clear owner
Reliability backed by SLAs
Manage complex data estates with consistent governance, trust, and visibility.
Multi-cloud governance at scale
Customer 360 trust and lineage
Pipeline monitoring and observability
Cross-domain KPI standardization
Give every data team one place to catch, triage, and resolve data issues.
End to end pipeline monitoring
Automated anomaly detection
Downstream impact and incident triage
Reliable data for analytics and AI
Trusted by organizations operating under OJK, BNM, MAS, and APRA regulatory frameworks across APAC.
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Rated 4.6/5 on
Automation of Monitors
Data Lineage
Their data contract module is amazing which virtualises and runs monitors.
Big fan of their UI/UX, it simple but managing all the complex task.
My team uses on a daily basis.
Seamless integration with all the data connectors. We also liked the new dbt-core connector directly integrated with Object storage.
Automated Column-Level lineage
Perfect blend of Data Catalog and Data Observability modules.
Business users are able to understand if the reports /dashboard have issues / incidents.
Personally liked the monitors by segment since we have mulitple business it provides incidents breakdown by attributes.
UX and UI, features, flexibility and excellent customer service. People like Manoj Matharu took the time to understand my business and data needs before trying to solution.
One of the best-designed data products. Our complete data infra is getting observed and governed by decube. My fav is the lineage feature which showcases the complete data flow across the components.
What I appreciate most about Decube is its intuitive design and the way it supports maintaining data trust. The platform allows for straightforward monitoring of data quality, making it easier to detect issues early on.One of the most valuable aspects is the transparency it brings to our data pipelines, which also streamlines collaboration among teams. The greatest benefit is the assurance that our data remains accurate, consistent, and prepared for decision-making, all without the need to spend countless hours troubleshooting.
Decube is packaged of solution for us. We were struggling to find one good tool in which we can intigrated with our existing data stack we are using mysql. As a DevOps we used to write crond jobs to check data quality but when we adapt this tool the work and quality both are improved. I highly recommend !
Data Observability refers to the ability to monitor, understand, and ensure the health of data across pipelines, systems, and business applications. It focuses on proactively identifying data quality issues, anomalies, schema changes, and lineage gaps before they impact business decisions or AI models.
Poor data quality can lead to incorrect insights, failed machine learning models, and compliance risks. Data Observability ensures trust in data by continuously monitoring pipelines, detecting anomalies, and giving end-to-end visibility into how data flows through your ecosystem.
Data Quality focuses on measuring attributes like accuracy, completeness, and consistency. Data Observability goes beyond this by providing real-time monitoring, lineage tracking, and root-cause analysis across the entire data stack. Together, they create a reliable foundation for AI and analytics.
Freshness – Is data arriving on time?
Volume – Are data records complete?
Schema – Has the structure changed unexpectedly?
Lineage – Where does the data come from and how is it transformed?
Quality metrics – Is the data correct and usable for business needs?
ROI can be measured by:
Reduction in downtime and failed pipelines
Faster issue resolution (MTTR – Mean Time to Resolution)
Increased trust in analytics and AI models
Compliance cost savings
Improved business decision-making
Decube uses configurable thresholds (absolute, percentage, or auto), severity levels, and routed alerts (Slack, Microsoft Teams, email, webhook, Jira), so you only get the alerts that matter.
dbt tests are manual and reactive. Decube adds ML anomaly detection for freshness and volume, always-on schema-drift detection, custom SQL monitors, and incident management with impact analysis.
Monitoring tells you a specific metric crossed a threshold; observability lets you understand why across pipelines, with lineage and root-cause context, so you can trace an issue to its source and downstream impact.
Book a 30-min demo with us and see how lineage, quality, governance, and compliance come together in a single platform.
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