Teams should treat the data lake as a central raw-data layer, then use an observability pipeline to filter, transform, and route logs, traces, and metrics to the right tools. This preserves flexibility when sources change, reduces re-architecture work, and supports broader analysis across historical data. The key is to separate ingestion, storage, and consumption cleanly.
Why a Data Lake Helps Without Forcing a Telemetry Rebuild
A data lake works best here as a decoupling layer, not as a replacement for every observability tool you already run. Raw logs, traces, and metrics can land once, then be shaped for multiple consumers later, which avoids hard-coding one vendor’s schema or retention model into the ingestion path. That separation is what lets teams evolve sources and analysis independently.
Operationally, this means you preserve the existing telemetry stack where it adds value, while using the lake for retention, historical analysis, and cross-domain correlation. The observability pipeline becomes the control point for normalization, enrichment, sampling, and routing, so teams can change back-end tools without redoing collection at the edges.
When this pattern is done well, the lake also reduces the pressure to over-index on perfect upfront modelling. Teams can keep higher-fidelity raw data for later investigation, then derive tool-specific views for search, dashboards, alerting, or offline analytics. That flexibility is especially useful when application teams, infrastructure teams, and security teams all need different slices of the same telemetry.
What the Pipeline Should Own, and What It Should Not
The key design choice is to keep ingestion, storage, and consumption separate. Ingestion should focus on reliable collection and transport. Storage should preserve the original events with enough context for later reprocessing. Consumption should be where format conversion, filtering, indexing, and destination-specific enrichment happen, so you can change one layer without destabilising the others.
That also means resisting the temptation to let every tool become a destination of record. If dashboards, SIEM workflows, and analytics jobs all depend directly on the same live pipeline, you create brittle coupling and duplicate transformation logic. A cleaner model is to send raw telemetry to the lake, then publish purpose-built downstream views for the systems that need them.
This architecture is most effective when the pipeline can handle schema drift and source churn. New services, new fields, and new instrumentation should be treated as normal rather than as a reason to rebuild the platform. The observability layer should absorb that change and expose a stable contract to consumers, even if the underlying producers evolve quickly.
For teams working with long-lived evidence and investigations, that historical layer matters. A lake makes it easier to re-run queries across older data after an incident, compare current behaviour with baseline periods, and correlate signals that were not originally ingested into the same tool. That is where the architectural payoff becomes visible: less re-architecture, more reuse of existing telemetry.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST CSF 2.0, CIS Controls v8 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.RM-01 — Risk Management Strategy | Telemetry centralisation changes observability risk and change tolerance. |
| DE.CM-01 — Continuous Monitoring | A data lake improves ongoing visibility across logs, traces, and metrics. | |
| PR.PT-01 — Protective Technology | Pipeline controls protect telemetry integrity while separating ingestion from consumption. | |
| Recommendation — Define telemetry decoupling as part of your risk strategy for tool and source changes. Maintain continuous monitoring by routing normalised telemetry into reusable analytic views. Use protective controls in the telemetry pipeline to preserve integrity and availability of observability data. | ||
| CIS Controls v8 | 8.2 — Audit Log Management | Raw telemetry retention and reuse depend on disciplined log handling and storage. |
| 8.8 — Audit Log Access Control | A lake-based observability model needs controlled access to sensitive telemetry stores. | |
| 15.4 — Log Storage and Retention | The pattern relies on durable storage of original telemetry for replay and historical analysis. | |
| Recommendation — Centralise audit and telemetry collection so raw events remain available for later analysis. Restrict access to telemetry repositories and derived views by role and purpose. Retain raw telemetry long enough to support incident review, trend analysis, and reprocessing. | ||
| NIST AI RMF | MAP-2 — Map Context | The same telemetry supports multiple consumers, so context mapping is essential. |
| GOV-3 — Risk Management Culture | Decoupled observability reduces operational friction when sources and tools change. | |
| Recommendation — Map data flows and stakeholders before routing telemetry into shared analytical layers. Treat observability architecture as a governed capability with explicit change tolerance goals. | ||
Practitioner Guidance
What to prioritise: Define the lake as the durable raw-data tier and make the pipeline the only place where telemetry is filtered, normalised, or re-routed. If transformation logic is already embedded in multiple tools, consolidate it before adding more sources.
What to verify: Confirm that raw events can be replayed or reprocessed without loss of essential context, and that downstream tools can be swapped without changing source instrumentation. If a consumer cannot tolerate a schema change, isolate that dependency behind a derived view rather than pushing the constraint upstream.
Common mistake: Treating the lake as just another sink for finished data. That erodes the main advantage of the pattern, because teams end up duplicating transformations and losing the ability to reanalyse historical telemetry with new questions or new tooling.
Practitioner takeaway: The goal is not to centralise every observability function, but to centralise the raw data and keep the analysis surface modular so tool changes do not force telemetry rework.
Related resources from NHI Mgmt Group
- How should teams instrument Java LLM applications for observability without rebuilding their monitoring stack?
- How should security teams use streaming security data to improve detection without flooding downstream tools?
- How should security teams use MCP to improve decision speed without weakening data governance?
- How should security teams use security data pipeline platforms to improve SOC detection without overwhelming downstream tools?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 19, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
