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New connector: Azure Data Factory
Integrate Azure Data Factory with Decube’s Data Catalog. Monitor job statuses, investigate incidents, and visualize data lineage efficiently.
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New
Proof for Regulators. Context for AI. Now One Product.
Kindly fill up the following to try out our sandbox experience. We will get back to you at the earliest.
Ensure robust and efficient data pipelines with end-to-end observability.
Track the performance of Airflow, DBT, and Fivetran related to their efficiency, accuracy, and reliability, and identifying any potential areas for improvement.
Monitor ETL jobs with ease in the Asset Details Overview where the status of each job is shown in real-time. Want instant failure alerts? Connect our platform to MS Teams or Slack.
Monitor every job and run, catch SLA breaches, and route alerts before downstream teams notice.
Track the status of every job and run across your orchestrators.
Know the moment a run is late or breaches its SLA.
See past runs, durations, and failures at a glance.
Route alerts to Slack, Teams, email, or PagerDuty.
Trace a failure to its source across the pipeline.
Confirm data landed on time for downstream teams.
Connect Airflow, dbt, Fivetran, and your warehouses in minutes.
Decube tracks the status, timing, and history of every job against your SLAs.
Get routed alerts with run details and downstream impact, and fix issues fast.
Two layers of trust that work together. Decube covers both.
Watches the pipelines and jobs that move your data: did the run succeed, on time, within SLA.
Watches the data itself: freshness, volume, schema, and quality across your tables.
Decube gives you both, so you know the pipeline ran and the data it produced 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 pipelines need monitoring you can trust.
Pipelines fail silently and you find out from stakeholders.
Jobs run late and you miss your data SLAs.
There is no history of which runs failed and why.
A failed job breaks downstream reports with no warning.
Alerts are noisy or go to the wrong team.
Root cause of a pipeline failure takes hours to find.
Built for teams where late or broken pipelines have real consequences, and a silent failure isn't caught until a stakeholder complains.
Keep regulated reporting pipelines on time and within SLA.
SLA monitoring on critical reporting pipelines
Alerts the moment a job runs late or fails
Status across dbt, Airflow, and warehouse jobs
Faster recovery with clear failure context
Make sure actuarial and claims pipelines deliver on schedule.
Freshness and timing checks on every run
Routed alerts for late or failed jobs
A dependency view across upstream and downstream
Fewer surprises in daily and monthly close
Monitor high-volume ingestion pipelines across many systems.
Job and run monitoring at scale
SLA tracking across billing and network data
Alerts routed to Slack and Teams
Quick root cause on broken pipelines
Keep hundreds of jobs healthy and on time across the stack.
The status of every job and run in one place
SLA and freshness monitoring across the stack
Alerts before stakeholders notice a problem
Dependency mapping for faster triage
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 !
Pipeline observability is the continuous monitoring of your data jobs and pipelines, their status, timing, run history, and failures, so you catch broken or late pipelines before they produce stale or missing data downstream.
Pipeline observability watches the jobs that move data: did the run succeed, on time, without errors. Data observability watches the data itself: is it fresh, complete, and correct. Decube provides both, and links a failed run to the exact tables it affected.
Decube monitors jobs across Google Big Query, Tableau, Fivetran, dbt, Azure Data Factory, Airflow and many more.. showing each job's status and run history in one place so you do not check each tool separately.
Decube shows each ETL job's status in real time in the Asset Details Overview and sends instant failure alerts to Microsoft Teams or Slack, so the right people know before stakeholders notice missing data.
By watching job timing and run history, Decube flags pipelines that are late or failing before they breach an SLA, so you fix the cause during the window instead of explaining a missed delivery afterward.
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.
In Decube a failed or late run links to the assets and column level lineage downstream of it, so you see which tables, dashboards, and consumers are impacted and can prioritize the fix, not just that a job turned red.
Decube connects to your existing sources and orchestrators and starts populating job status and run history automatically, so you get pipeline visibility without instrumenting each pipeline by hand.
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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