_For%20light%20backgrounds.svg)
New
Beyond the Spaghetti Graph. Introducing the new Lineage Canvas.
Kindly fill up the following to try out our sandbox experience. We will get back to you at the earliest.
See what poor data quality is costing your team. Adjust your data estate size and location to model annual savings across six key value drivers.
.webp)
Write your own tests with SQL scripts to set up monitoring specific to your needs.
Find where the incident took place and replicate events for faster resolution times.
Enable monitoring across multiple tables within sources by our one-page bulk config.
Choose which fields to monitor with 12 available test types such as null%, regex_match, cardinality etc.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam fermentum ullamcorper metus ac egestas.
Thresholds for table tests such as Volume and Freshness are auto-detected by our system once data source is connected.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam fermentum ullamcorper metus ac egestas.
Alerts are grouped so we don't spam you 100s of notifications. We also deliver them directly to your email or Slack.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam fermentum ullamcorper metus ac egestas.
Always experience missing data? Check for data-diffs between any two datasets such as your staging and production tables.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam fermentum ullamcorper metus ac egestas.
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 !
Industry research estimates that poor data quality costs organisations an average of $12.9 million per year. Costs stem from engineering time spent debugging pipelines, resolving data incidents, manual documentation, compliance reporting, and broken dashboards reaching business stakeholders. Decube's ROI Calculator models these costs based on your specific team size, data estate, and geography.
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.
Organisations using Decube typically see returns across six value drivers: faster data incident resolution (early detection before downstream impact), reduced time hunting for datasets, automated lineage for root cause analysis, AI-assisted data documentation, streamlined compliance and audit reporting, and fewer broken dashboards. The ROI multiple varies based on team size and data estate but commonly exceeds 5–10× annual platform cost
Decube's data observability layer monitors tables and pipelines in real time, catching approximately 40% of data incidents before they reach downstream dashboards or AI models. For incidents that do occur, automated lineage reduces mean time to resolution from an average of 6 hours to under 2 hours — cutting the cost per incident by over 65%.
On average, data and analytics team members spend 3.5 hours per week searching for the right dataset and validating it for use, and a further 2.5 hours per week manually writing or updating table and column documentation. Decube's catalog and AI-assisted documentation features reduce these overheads by 65% and 70% respectively, reclaiming hundreds of hours per year per team.
.svg){kind=small}
