Ops Pillar · Data Ops

Every data pipeline, watched and optimized.

Data Ops monitors every kind of data pipeline, batch, streaming and on-demand, in one place. With built-in data connectivity, it tracks execution health, forecasts capacity, recommends optimizations and enforces SLAs, rules and lineage, then hands the picture to Sentinel AI to act.

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Ingestion & CDC
Batch, streaming & on-demand
Capacity forecasting
AI optimization
SMART governance
Built-in data connectivity

Today's activityAll pipelines

BatchRunningHealthy

StreamingOn-timeFlowing

On-demandRunningHealthy

AlertsClearNone

Cluster summary

Executions healthy

Most runs completing successfully across all queues.

A few delayed

Some runs behind schedule and worth a look.

Capacity headroom

Compute and memory comfortably within limits.

What is Data Ops

Operations for every data pipeline you run.

Data teams run batch jobs, streaming flows and on-demand workloads across many engines, and lose sight of what is healthy, what is late, and what is costing too much. Data Ops monitors all of them in one place, from ingestion onward, with unified execution health, capacity forecasting, AI-powered optimization and SMART governance for SLAs, rules and lineage.

Ingestion & change data capture

Bring data in, and keep it in sync.

Data Ops includes big-data-scale ingestion from diverse sources. Build ingestion flows with a guided wizard, deploy them through an approval-governed process, and keep targets continuously in sync with Change Data Capture, log-based, query-based and trigger-based, replicating inserts, updates and deletes in real time.

Guided flow builder for big-data-scale ingestion
Real-time change data capture: log, query and trigger based
Approvals, SLAs and monitoring on every ingestion flow

Ingestion & CDCFlows

FLOWSconfigured & approvedApproved

DEPLOYdeployments active & replicatingLive

Unified execution health

Batch, streaming and on-demand, one view.

See every pipeline type in a single operational view: what is running, what succeeded, what failed, what is delayed, and where alerts are firing. An execution-distribution view maps runs across queues so operational and core-processing workloads stay clearly separated and easy to reason about.

Batch, streaming and on-demand executions together
Success, failure, delay and alert status at a glance
Execution distribution across queues, in real time

Execution healthLast 24h

BATCHrunning · most succeedingHealthy

STREAMflowing · on-timeFlowing

ON-DEMANDrunning · completingHealthy

DELAYEDa few runs behind scheduleWatch

Capacity & forecasting

Know your capacity before it bites.

Track compute, memory and throughput across your pipeline infrastructure in real time, and use ML-powered forecasting to see where demand is heading. Plan capacity on evidence, catch anomalies early, and avoid the queue pressure that turns into missed SLAs.

Real-time compute, memory and throughput utilization
ML-powered demand forecasting for capacity planning
Early anomaly detection on resource consumption

Capacity & forecastInfrastructure

ComputeModerate

MemoryModerate

Throughput trend & forecast

AI optimization

Recommendations that cut cost and speed runs.

Data Ops studies how your pipelines actually run and suggests specific, ranked improvements: reclaim under-used resources, tune memory, optimize slow queries, and fix the patterns that quietly burn compute. Track generated versus implemented recommendations so savings are visible, not theoretical.

Cost and performance recommendations, ranked by impact
Resource allocation, memory and query tuning
Generated vs implemented tracking, with savings breakdown

Active recommendationsAI-powered

CRITICALresource under-allocated on a heavy jobCost

MEDIUMquery optimization opportunityPerf

MEDIUMmemory tuning for a streaming flowPerf

APPLIEDreclaimed idle capacitySaving

SMART governance

SLAs, rules and lineage, enforced.

SMART is Data Ops' governance framework across the whole pipeline lifecycle: SLA enforcement, Monitoring of every state transition, automated Actions, business and regulatory Rules, and end-to-end Traceability. Pipelines run reliably, respond to issues on their own, and stay audit-ready.

SLA thresholds with automatic corrective actions
Business and regulatory rule validation
Complete data lineage and audit readiness

SMART governanceLifecycle

S · SLAperformance thresholds enforcedOn

M · MONITORstart, end and state transitionsLive

A · ACTIONautomated responses on breachArmed

R · RULESbusiness & regulatory validationPassing

T · TRACEend-to-end data lineageComplete

Executions & queues

Drill into every run and every queue.

Explore running and completed executions across all workloads, and see how compute and memory are distributed across queues. Spot capacity pressure, idle queues and resource imbalance at a glance, and open any flow or deployment to follow it end to end.

Real-time explorer for running and completed runs
Per-queue compute and memory availability
Drill into flows and deployments end to end

Queue utilizationAll queues

Core processing

ml-training

Analytics

Ingest

Operational

07Real-world use cases

Built for the pipelines you already operate

These are not demo scenarios. Each one is a situation Data Ops teams meet in real operations, and each ends the same way: an investigation that starts at the evidence and finishes with a decision someone can defend.

A nightly batch runs long

Execution health and queue distribution show where the run actually stalled.

Root cause before morning

Cost creeps across pipelines

Ranked recommendations for allocation, memory and query tuning.

Savings tracked to realised

A regulator asks about a field

Complete lineage and audit trail across every ingestion and transform flow.

Answered from the platform

A CDC stream falls behind

Log, query and trigger-based capture monitored with approvals and SLAs.

Sync gap caught early

08Business perspective

What it is worth outside the data team

Data Ops is an engineering capability with a business ledger. The entries that matter are fewer sla breaches, lower pipeline cost, predictable capacity and lineage on demand, and it moves all of them.

Fewer SLA breaches

Thresholds act automatically before downstream teams notice.

Lower pipeline cost

Ranked tuning recommendations with savings tracked to realised.

Predictable capacity

ML forecasting replaces mid-run compute and memory surprises.

Lineage on demand

Every field traceable end to end when a regulator asks.

09The numbers

The cost of reruns, summed honestly

Worked example for a mid-size data estate. Every figure shows the sum it came from; during a pilot we substitute your own inputs.

Engineer hours on reprocessing

3,024 hrs

12/mo × 14 jobs × 2 hrs × 12

That is standing headcount

1.7 FTE

3,024 hrs / 1,800 productive hrs per FTE-year

Annual cost of reruns

$257k

3,024 hrs × $85/hr fully loaded

Released if breaks halve

$129k

Modelled at 50% reduction, not a measured result

Data Ops sees. Sentinel acts.

Data Ops does more than show a late pipeline or a starved queue. Every signal, execution health, capacity, recommendations and SMART governance, feeds Sentinel AI, the intelligence component at the core of Opstral, which resolves issues through governed, reversible Action Tickets.

Retry a failed run, reallocate a queue, apply an optimization, every step explained with citations and fully audited, on data that flows through the platform.

Observe
Data Ops correlates execution health, capacity, recommendations and SMART governance signals.
Investigate
Sentinel AI finds the root cause across pipelines and queues and selects the right procedure.
Act
ProcBot executes the approved MOP, retry, reallocate, tune, through a reversible Action Ticket.
Optimize
Sherlock validates the outcome and feeds the learning back to prevent the next SLA breach.

See Data Ops on your own pipelines.

Book a walkthrough and see unified pipeline health, capacity forecasting, AI optimization and SMART governance on data that looks like yours.

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