Catch broken databefore users notice.
Data Quality Monitoring watches your pipelines and data tests, alerts your data team when something fails, and traces lineage, so after an incident you know which systems and whose data were hit.
- Alerts before users notice
- When a pipeline misbehaves or a data test fails, your data team gets the alert first, instead of hearing it from a business user.
- A 24-hour impact view
- Lineage is traced across the monitored pipelines, and the platform is built so that within 24 hours of an incident you know which systems and whose data were hit.
- Tested before go-live
- Rules, alerts and dashboards are tested at row and component level, then accepted by your users, before they go live.
The problem
Your first alert is a colleague's complaint
A pipeline stops, a data test fails or a source delivers incomplete data, and nothing alerts the data team. The first signal is a colleague asking why a report looks wrong. Engineers then trace the cause backwards by hand, nobody can say quickly which systems or whose data were hit, and trust in the numbers drops further with every incident.
Common symptoms
Business users find broken data before your data team does
A failed pipeline or data test goes unnoticed until a report looks wrong
Key datasets have no clear owner, so every issue bounces from team to team
What this costs you
Engineers spend hours tracing each failure by hand instead of building the roadmap
Trust in the data drops, and teams start keeping their own copies and checks
Without lineage, working out what an incident damaged turns into guesswork across systems and teams
How Data Quality Monitoring works
Catch the failure, know the impact
Monitoring only helps when the alert reaches the right people with enough context to act. The platform runs your data tests, alerts the data team when a pipeline or test fails, and records lineage and ownership. After an incident, the conversation is about the fix, not about where the problem started.
Quality rules and data tests
Focus
Agree with your data owners which checks matter, such as completeness, freshness and agreed definitions, and run them automatically on Azure Databricks.
Key outputs
Agreed quality rules, automated data tests and a test history per dataset.
Alerts and data health
Focus
Alert the data team whenever a pipeline misbehaves or a test fails, and show the status of every monitored dataset in one view.
Key outputs
Alert rules and recipients, a data-health dashboard in Power BI or Grafana, and an agreed follow-up for each type of alert.
Lineage and ownership
Focus
Record where each dataset comes from, where it goes and who owns it, so after an incident you can see which systems and whose data were hit.
Key outputs
Lineage across the monitored pipelines, an owner for each key dataset and an impact view for incidents.
Capabilities
What we deliver
What the package delivers: checks that run on their own, alerts that reach the data team, and the context to act on them.
Pipeline monitoring
Watches your existing pipelines, on Azure Databricks or another platform, and flags runs that fail or behave unexpectedly.
Automated data tests
Quality rules that run against your data automatically, themselves tested at row and component level before go-live.
Alerts for the data team
An alert when a pipeline misbehaves or a test fails, so problems surface in the data team first.
Data-health dashboards
Dashboards in Power BI or Grafana that show the state of each monitored dataset and its recent test results.
Data lineage
Lineage across the monitored pipelines, with a named owner for each key dataset, so the impact of an incident is traced, not guessed.
Training for your team
Courses in Databricks and Power BI, so your own people can maintain the rules, alerts and dashboards.
Where it creates value
Who benefits most
Practical value for the teams that carry the most complexity.
Head of data / CIO
Data leaders accountable for a platform the business has to be able to trust.
- Failures reach the data team first, not a board report
- One view of the health of monitored pipelines and datasets
- Less engineering time lost to tracing failures by hand
Data engineering
Engineers who build and run the pipelines and now hear about failures from users.
- An alert when a pipeline or test fails, with the affected dataset in view
- Lineage that shows what sits downstream of a failure
- Tests and alert rules maintained as part of the platform
Finance and control
Finance teams that need to know a figure is complete and current before it goes into a report.
- Quality checks on the datasets behind financial reporting
- Clear notice when a source is late or incomplete
- A traceable route from a reported figure back to its source
BI and analytics
Analysts and report builders who depend on fresh, correct data.
- Data-health status visible before a report is refreshed or shared
- Freshness checks that show when data is too old to act on
- A known owner to contact when a dataset looks wrong
Audit and risk
Audit and risk teams that ask where a number comes from and who is responsible for it.
- Lineage that shows where monitored data comes from
- A named owner for each key dataset
- A history of test results per dataset
Less relevant if...
Data Quality Monitoring is not the right first step in these situations.
- There are no production pipelines to monitor yet
- Your main need is access rights, data classification or GDPR controls
- You want SolvX to watch and fix a solution it built for you, rather than alerts for your own data team: see Managed Services
How it works
A practical delivery approach
Every engagement follows a clear path from diagnosis to delivery and handover.
Scope
On site, we agree with your data team and data owners which pipelines and datasets matter most, what counts as a failure, and who should receive an alert.
A scope document with the datasets to monitor, the first quality rules and the alert recipients.Design
We design the checks, alert routes, lineage and dashboards on Azure Databricks and Azure Function Apps, fitted to your existing platform.
A monitoring design, a rule catalogue and the dashboard layout, agreed before the build starts.Build and test
We build the tests, alerts, lineage and data-health dashboards, and test them at row and component level against your real pipelines.
Working monitoring for the pipelines in scope, with the unit test results.Acceptance
Your data team and the data owners test the alerts and dashboards against agreed scenarios, such as a failed load or a failing test, before go-live.
Signed-off acceptance tests and a go-live decision.Hypercare and handover
After go-live, the senior experts who built the monitoring stay available for questions, and we train your team in Databricks and Power BI so they can run and extend it.
Handover documents, a trained team and, if you want one, an SLA for ongoing support.
Proof & trust
Results that speak for themselves
Alerts before users notice
When a pipeline misbehaves or a data test fails, your data team gets the alert first, instead of hearing it from a business user.
A 24-hour impact view
Lineage is traced across the monitored pipelines, and the platform is built so that within 24 hours of an incident you know which systems and whose data were hit.
Tested before go-live
Rules, alerts and dashboards are tested at row and component level, then accepted by your users, before they go live.
Example engagement
What it looks like when it works
How an engagement like this typically runs. An illustration, not a client result.
Context
Example: a large organisation with a central data platform on Azure Databricks, where finance and operations reports depend on nightly pipelines and failures are usually reported by the people reading those reports.
Challenge
The data team hears about broken loads from report users, and every incident starts with manual tracing to find out which tables, reports and teams are affected.
Solution
Scoping on site to select the critical pipelines and datasets, quality rules and alerts built and tested on the existing Databricks platform, lineage recorded for the monitored flows, and a data-health dashboard in Power BI, followed by acceptance testing, training and hypercare.
What you get
Automated quality tests
Agreed rules for the critical datasets, run automatically and tested before go-live.
Data team alerts
A message to the data team whenever a pipeline misbehaves or a test fails.
Lineage impact view
Lineage for the monitored flows that shows which systems and whose data an incident hit.
Context
Example: an organisation whose key datasets are shared by several departments without an agreed owner, so data issues bounce from team to team.
Solution
An owner recorded for each key dataset, quality rules agreed with those owners, alerts to the data team with the owner in view, and a Grafana dashboard that gives engineers the state of every monitored dataset.
What you get
Owner per key dataset
A record of who owns each key dataset, agreed with the business and shown with every alert.
Agreed quality rules
Rules set with the dataset owners, so a failed test points to something the business has agreed matters.
Grafana health dashboard
An engineering view of failed tests and pipeline runs across the monitored datasets.
Frequently asked questions
Your questions, answered
Your pipelines and your data. The platform checks whether pipelines run as expected and whether the data passes the quality tests you agree with your data owners. When either fails, the data team gets an alert with the affected dataset and its owner in view.
The platform is built so that within 24 hours of an incident you know which systems and whose data were hit. Lineage makes that possible: it shows what sits downstream of a failed pipeline or test.
The monitoring itself runs on Azure Databricks and Azure Function Apps, with data-health dashboards in Power BI or Grafana. It monitors the pipelines and data sources you already run, standard or custom-built. Where no standard connector exists, we build one. Which ones are connected is agreed during scoping. Azure and Power BI run on your own Microsoft bill.
A sponsor, usually the head of data or CIO, access to the pipelines and platform in scope, time from the engineers who run them, and data owners who decide which checks matter and take part in acceptance testing.
The build is bought as fixed-price phases, each set after scoping and ending with live monitoring, a demo and handover documents. After hypercare, support can continue under Managed Services, as an SLA or a subscription. Further changes can be billed by the hour. You receive a price once the scope is clear.
You own your data, your client-specific code and the documentation. SolvX keeps the rights to its generic building blocks; the contract sets out the details. You receive handover documents at the end of each phase, and our training and pairing programme aims for three people on your side who can run each critical component. During hypercare, our senior experts stay available for questions.
It covers the parts of governance that monitoring depends on: lineage and a named owner for each key dataset. Access rights, data classification and GDPR controls are not part of this package.
When there are no production pipelines to monitor yet, or when you want SolvX to watch and fix a solution it built for you, rather than alert your own data team. The second case is covered by Managed Services. In those cases we will tell you in the first conversation and suggest where to start instead.
Next step
Where do you want to start?
Pick the path that matches where you are today. Both begin with a conversation.
Start with the pipelines that matter most
Plan a scoping call. We look at your pipelines, recent incidents and key datasets, and outline a fixed-price first phase for monitoring that alerts your data team before your users notice.
- Find the pipelines and datasets where failures hurt most
- Agree which quality checks and alerts to start with
- Get the scope and shape of a fixed-price build phase
No obligation. Typical response within 1 business day.
Start with an assessment
Not ready to commit to a pilot? A 2 to 4 week assessment maps your current state and identifies the highest-value starting point, with no obligation to proceed further.
- Map your current situation and pain points
- Identify 2 to 3 high-value improvement opportunities
- Get a recommended pilot scope and expected outcomes
No obligation. Typical response within 1 business day.
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