Solutions
Products for seeing, running and improving your data platform.
Built from the problems we solve on client engagements: understanding dependencies, catching failures early, measuring engineering effort and removing manual incident work.
- Data lineage & impact analysisDataventra ImpactKnow what breaks before you change it.
- DataOps & ETL monitoringDataventra MonitorSee every job, every run, every breach.
- Engineering & Jira analyticsDataventra WorkTurn Jira activity into delivery insight.
- SQL & SSIS failure responseAutomated Incident ManagementFrom failed job to actionable ticket — automatically.
Dataventra Impact · Data lineage & impact analysis
Know what breaks before you change it.
Impact maps every dependency from source system to report, so you can see exactly which tables, views, datasets and reports are affected when a table, column or pipeline changes.
A column is renamed in the warehouse. Two days later, finance asks why a board report is blank. Nobody knew they were connected.
End-to-end lineage
Source systems → source tables → staging → warehouse → views → datasets → reports, mapped as one connected graph.
Downstream impact analysis
Select any table, column or pipeline and see every downstream object and business asset it feeds.
Change risk before deployment
Review the blast radius of a schema change during design, not after a failed refresh.
Ownership & accountability
Attach owners to reports and datasets so the right people are informed when an upstream object changes.
Proposed change
Column CustomerSegment renamed → SegmentCode
- Views
- 2
- Datasets
- 2
- Reports
- 3
Impacted objects
- View: vw_CustomerRevenue
- View: vw_SegmentMargin
- Dataset: Sales Performance
- Dataset: Customer 360
- Report: Regional Sales
- Report: Segment Margin
- Report: Account Review
- Changed object
- Impacted downstream
- Not affected
Owners to notify: Finance BI, Sales Operations
Dataventra Monitor · DataOps & ETL monitoring
See every job, every run, every breach.
Monitor gives data teams one operational view of SQL Agent jobs, SSIS packages and pipelines — failures, SLA breaches, runtimes, record counts and data freshness, with the history to spot trends early.
An SSIS package fails at 2 a.m. The first alert is a business user at 9 a.m. asking why yesterday's numbers are missing.
Job & package execution
Track SQL Agent jobs, SSIS packages and pipeline runs with status, step-level detail and error output.
SLA & freshness tracking
Define when data must be ready and get flagged the moment a load is late, stale or at risk.
Runtime & volume trends
Compare runtimes and record counts against history to catch slow degradation and silent data loss.
Source-system activity
Understand when upstream systems deliver, so delays are traced to their origin instead of guessed at.
Runs today
128
Failed
1Failed
SLA at risk
1SLA at risk
Last warehouse load
05:42
| Job / package | Status | Runtime | Rows |
|---|---|---|---|
| DW_Nightly_LoadSQL Agent | Succeeded | 42m 10s | 3,214,880 |
| SSIS_Stg_ERP_CustomerSSIS | Succeeded | 6m 48s | 184,220 |
| SSIS_Stg_CRM_AccountSSIS | Failed | 1m 12s | — |
| Fact_Sales_IncrementalSQL Agent | Running | 18m 03s | 1,106,432 |
| PBI_Refresh_SalesDataset | SLA at risk | — | — |
| DQ_Checks_FinanceSQL Agent | Succeeded | 2m 31s | 48 checks |
Runtime trend · DW_Nightly_Load (minutes)
Runtime anomaly · 58m run
Dataventra Work · Engineering & Jira analytics
Turn Jira activity into delivery insight.
Work turns Jira data into a clear view of engineering effort — hours by developer and project, estimated vs actual, sprint performance, capacity and delivery trends.
Every project is 'on track' until it isn't. The data to see it coming was in Jira all along — it just never became a report.
Effort & allocation
Hours by developer, team and project, so you can see where engineering time actually goes.
Estimated vs actual
Compare estimates with logged effort by project, epic and issue type to improve future planning.
Sprint performance
Committed vs completed, carry-over and scope change across sprints — trends, not snapshots.
Capacity & delivery trends
Team capacity against demand, and delivery trends over time, for resourcing decisions grounded in data.
Sprint 24 · Committed
58 pts
Sprint 24 · Completed
51 pts
Sprint 24 · Capacity used
87%
Estimated vs actual hours
- Estimated
- Actual
| Project | Hours | Estimated | Actual | Variance |
|---|---|---|---|---|
| Warehouse modernisation | 320h | Actual 356h | +11% | |
| SSIS migration | 240h | Actual 228h | -5% | |
| Power BI rollout | 180h | Actual 205h | +14% | |
| Data-quality framework | 120h | Actual 118h | -2% | |
| Platform support | 160h | Actual 190h | +19% |
Automated Incident Management · SQL & SSIS failure response
From failed job to actionable ticket — automatically.
When a SQL or SSIS job fails, the failure is detected, the execution analysed and the error captured. A Jira ticket is raised with the diagnostics attached, and the responsible team is notified.
A job fails. Someone notices, digs through logs, copies an error into a ticket and chases the right team. Every step is manual, and every step is delay.
Failure detection
Failed SQL Agent jobs and SSIS executions are picked up as they happen — not at the next manual check.
Execution analysis
The failing step, error message, runtime and record counts are captured automatically.
Jira ticket generation
A ticket is created with diagnostics attached, routed to the right project and priority.
Team notification
The responsible team is notified with context, so resolution starts with information, not investigation.
Failure detected02:14:07
SSIS_Stg_CRM_Account · step “Load Account”
Execution analysed02:14:09
Runtime 1m 12s · 0 of 96,410 rows committed
Error captured02:14:10
Violation of PRIMARY KEY constraint 'PK_stg_Account'
Jira ticket created02:14:12
DATA-1287 · Priority High · Data Platform
Team notified02:14:13
Data Platform on-call · with ticket link
SSIS_Stg_CRM_Account failed — PK violation on stg.Account
- Assignee
- Data Platform
- Source
- Automated
Diagnostics attached
- execution_log.txt
- error_detail.json
- row_counts.csv
- downstream_impact.txt
AI roadmap
Intelligence built on foundations, not hype.
Machine learning, generative AI and agents are where we're heading. We're building towards them deliberately — starting with the lineage, execution history and quality signals that make AI in data operations trustworthy.
- 01 · NowFoundation
Reliable, observable data
AI is only as good as the data and metadata beneath it. Lineage, execution history and quality checks are the foundation.
- End-to-end lineage metadata
- Execution and runtime history
- Data-quality and freshness signals
- 02 · NextIn development
Predictive data operations
Using execution history to anticipate problems instead of reacting to them.
- Runtime and volume anomaly detection
- SLA breach prediction
- Predictive analytics on warehouse data
- 03 · LaterExploring
Intelligent automation & AI agents
Generative AI and agents that understand your lineage and operations, and assist the engineers who run them.
- AI-assisted incident root-cause analysis
- Natural-language questions over lineage
- Agents for routine data-operations tasks
Roadmap items describe direction, not generally available features.
See how it would map to your environment.
We'll walk through Impact, Monitor, Work and incident automation against the systems you actually run.