Catch stale data before trust drops.
Detect lagging tables, delayed pipelines, missed SLAs, and ingestion gaps before downstream dashboards, analytics, and business decisions are affected.
OTEL-first. Vendor-neutral. Built around your stack.
Data observability built around the tools you already have.
DataObs helps engineering and data teams design and deliver data observability using OpenTelemetry and the observability platforms already available in their environment — Elastic, Grafana, OpenSearch, or a mixed stack. The aim is practical visibility into freshness, quality, lineage, volume, and trust without forcing a rip-and-replace platform decision.
ModelConsulting with reusable accelerators
BackboneOpenTelemetry plus existing tooling
FitAWS-native, platform-heavy, data-critical teams
Designed to fit your current observability stack.
DataObs is not built around a single monitoring vendor. The implementation approach uses OpenTelemetry as a portable telemetry layer and maps observability signals into the tools your teams already operate today.
Elastic
Grafana
OpenSearch
OpenTelemetry
AWS
What DataObs helps you see sooner.
Most teams do not need more disconnected dashboards. They need signals that explain whether data is late, broken, incomplete, drifting, or risky, and they need those signals integrated into engineering workflows and operational ownership.
Monitor the shape and validity of data.
Track completeness, consistency, rule failures, and key validation checks so teams know when the data is available but still unsafe to use.
Understand where failures spread.
Map upstream and downstream relationships to show impact radius, ownership, and which reports, services, or teams are exposed to a break.
Spot silent anomalies in flow and scale.
Catch row-count drops, spikes, partition gaps, and unusual movement patterns that often appear before a bigger data reliability incident becomes visible.
Use OpenTelemetry as the backbone, not as a constraint.
OpenTelemetry provides a common telemetry foundation for metrics, logs, traces, and events. That makes it a strong base for vendor-neutral observability design when clients already have different tools in place and want something interoperable, portable, and shaped by delivery reality rather than tooling ideology.
- Avoid unnecessary vendor lock-in.
- Reuse current observability investments.
- Standardise telemetry collection and routing.
- Build incrementally instead of replacing everything at once.
Currently supporting for AWS-native and platform-heavy teams.
DataObs fits best where infrastructure, pipelines, and data trust need to be monitored together — especially in AWS-native data platforms and mixed observability estates.
- Lambda, Glue, EMR, S3, Athena, RDS, EC2, and EKS.
- Airflow, dbt, Spark, streaming, CI/CD jobs, and batch workflows.
- Elastic, Grafana, OpenSearch, or mixed tooling.
- Teams that need practical answers, not another platform pitch.
A consulting model with reusable patterns.
DataObs is consultancy-led, but not one-off bespoke chaos. The model is to use repeatable OTEL-first patterns, practical health checks, observability design standards, and existing platform capabilities to build a solution that fits each client’s requirements and constraints.
Assess
Review current tooling, telemetry gaps, data platform flows, ownership boundaries, noisy alerts, and the points where failure is still detected too late.
Instrument
Add the right telemetry, checks, lineage signals, and data health logic using OTEL and the stack your teams already operate.
Operationalise
Turn the design into dashboards, alerts, triage paths, ownership models, and workflows that engineering and data teams can actually run with.
Data observability foundation
Start with freshness, quality, volume, and lineage around the datasets and pipelines that matter most.
Platform-aligned design
Connect data observability with infrastructure, job, and service signals so teams can reduce cross-layer blind spots.
Tool rationalisation
Improve signal design and telemetry flow using the observability tools the client already owns and understands.
Operational rollout
Define alerting, escalation, stakeholder views, and reporting so the work becomes part of daily operations, not shelfware.
Built on a wider observability model.
The broader DataObs vision fits inside a larger observability framework that connects full stack observability, pipeline observability, data observability, and business observability across one telemetry foundation.
What is happening in systems.
Infrastructure, APM, tracing, logs, and operational telemetry provide the platform context behind data incidents.
Are jobs and flows reliable.
Batch, streaming, CI/CD, orchestration, retries, and SLAs show whether the delivery layer is healthy and predictable.
Is the data trustworthy.
Freshness, quality, volume, schema, and lineage reveal whether downstream consumers should trust what they see.
What is the operational impact.
Business context helps teams prioritise incidents by customer impact, reporting risk, and real operational consequence.
FAQ
These are the first questions serious clients usually ask when they land on a specialist observability practice with an open, vendor-neutral approach.
Is DataObs a product or a consultancy?
DataObs is best positioned as a consultancy-led data observability practice with reusable OTEL-first implementation patterns, not as a closed platform that replaces a client’s current toolset.
Do clients need to adopt a new observability platform?
No. The recommendation is to work with the tools already available in the client environment wherever practical, then improve telemetry quality, consistency, and signal design around them.
Which platforms does this fit best?
The strongest fit is AWS-native data platforms and mixed observability estates using Elastic, Grafana, OpenSearch, Datadog, Kubernetes, and related monitoring stacks.
What should teams monitor first?
Freshness, quality, lineage, volume anomalies, and practical operating visibility are usually the best starting point because they directly reduce data downtime and trust erosion.
Primary action
Start a conversation
Build data observability around your requirements, not around a vendor pitch.
If your teams already have observability tools in place but still lack clear answers on data freshness, lineage, quality, and trust, DataObs can help design and deliver a solution that fits the environment you already run.
Start with a short architecture call.
Use this section for your real contact details, calendar link, and lead flow. The ideal first conversation is about current tooling, operating constraints, and where observability is still failing to answer the right questions.
Vendor-neutral data observability for AWS-native and platform-heavy environments
hello@dataobs.co.uk
GitHub — DataObs
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