Ask the Lake: The future answers back

Introduction

Most organisations still run observability like it’s 2013.

A dashboard lights up, an alert fires, someone opens a query window, teams scramble to understand what just happened, and then — eventually — a root cause is found.

Observability is still treated as a rear-view mirror , valuable only after something has gone wrong.

But modern systems don’t behave like they did a decade ago.

They are faster, more dynamic, more ephemeral.

Issues don’t politely wait for operators to ask the right questions.

Degradation appears, cascades, and disappears in minutes — sometimes seconds. Containers churn. Nodes autoscale. Infrastructure is liquid.

In this world, reactive observability is not enough.

We need something that can understand what is happening as it is happening , reason about it in context, and — critically — answer before we even finish forming the question.

This is the beginning of proactive observability.

This is where the future answers back.

And it starts when you Ask the Lake.

The Old Model: Answers Require Humans to Ask First

If you look at the last decade of monitoring and observability tools, you’ll see a very consistent pattern:

1. Data explodes
2. Teams drown
3. Dashboards multiply
4. Root-cause analysis becomes slower
5. Costs balloon
6. Operators burn out

The tooling has changed, but the operating model hasn’t.

It still assumes:

• humans know what to ask
• data has been indexed in advance
• analysis happens after the fact
• the cost of insight is acceptable
• insight must be pulled, not pushed

But in modern cloud-native systems — Kubernetes, serverless, service meshes, event-driven architectures — this model collapses.

Not because the tooling is inadequate.

But because the model itself is obsolete.

Every major company we spoke to at KubeCon said some version of the same thing:

“We don’t have an observability data problem. We have a ‘figuring out what to ask’ problem.”

And even when they do know what to ask, the data is scattered across vendors, pipelines, indexes, and SaaS layers — meaning the answer is always slower, more expensive, and less accurate than it should be.

The system is shouting, but the tools whisper.

Modern systems require a new approach — one built not around dashboards, nor indexes, nor queries, but around understanding.

This is why the Lake exists.

A New Behaviour Emerges: Systems That Understand Themselves

A Lake is not a database. It is not a log warehouse. It is not a metrics backend. It is not a tracing store.

A Lake is a living system — a unified, fresh, continuously updated telemetry surface where data is understood in place instead of being shipped, indexed, and queried after the fact.

When compute meets data at the source, insight no longer behaves like a report.

It becomes something closer to a conversation.

This is where something extraordinary happens:

When you Ask the Lake, the Lake answers back.

Not with a dashboard. Not with a query result. Not with a 40-line JSON blob.

But with understanding.

The Lake explains:

• what changed
• when it changed
• where the first signs appeared
• how it propagated
• what caused it
• what depends on it
• what will break next
• who is impacted
• why it happened
• why it matters

This is Observability Intelligence.

This is the foundation of AI-SRE.

This is the beginning of proactive systems.

And it shifts the role of observability from reactive tooling to an intelligent partner.

From Asking “What Happened?” to Hearing “Here’s What’s Coming.”

For decades, observability has been a game of catch-up.

Something breaks → alerts fire → operators react.

But the Lake alters the order of operations.

Instead of waiting to be asked, the Lake can proactively surface meaning:

• The error you’re seeing began 12 minutes earlier in an upstream service.
• This latency spike correlates with a retry storm in the API gateway.
• This deployment introduced a slow leak in memory — here are the services impacted.
• Your throughput is beginning to diverge from historical patterns.
• If this trend continues, user-impact will begin in 6–8 minutes.

These are not hypothetical.

They are the kinds of insights that become possible when telemetry is:

• unified
• fresh
• correlated
• reasoned in place
• continuously interpreted

This is the difference between raw data and a system that understands itself.

The KubeCon Insight: People Didn’t Know What to Ask — So We Showed Them

Nitish made an important observation at the booth:

“When we said ‘Ask the Lake,’ people didn’t know what they should ask.”

And this insight is gold, because it reveals the behaviour we’re disrupting.

Most engineers have been trained to think in terms of queries:

• “grep this log”
• “filter this trace”
• “show me CPU for the last hour”
• “what’s the error rate?”
• “which pod restarted?”

Queries are great.

But they require operators to know what they’re looking for.

In reality, the most valuable questions are the ones where operators don’t know what to ask yet.

The Lake fills this gap.

So let’s make it concrete.

Here are examples of questions that you can actually ask the Lake — and that the Lake can answer in real time:

Questions about change:

• What changed in my system in the last minute?
• What was the first signal of degradation?
• Which service introduced this behaviour?

Questions about causality:

• What is driving this spike?
• Which downstream systems are impacted?
• Is this deployment responsible?

Questions about correlation:

• What else is behaving abnormally right now?
• Show me signals that match this pattern.
• What depends on the service that’s failing?

Questions about prediction:

• What will break next?
• What trend is most concerning right now?
• If error rates continue, what happens?

Questions about business impact:

• Which customers are affected?
• Which transactions failed?
• Is this impacting revenue paths?

These are not dashboard questions. These are understanding questions. This is where the future begins to answer back.

Why This Matters: Insight Must Be Push-Based, Not Pull-Based

Observability has always relied on human pull:

• “Show me the logs.”
• “Run this query.”
• “Filter this data.” But systems now operate at a pace that humans cannot match.

A delay of even a few minutes can mean:

• noisy outages
• brownouts
• slow burn degradation
• missed SLOs
• compliance failures
• user churn
• revenue impact

Proactive observability is not a nice-to-have; it is a requirement for modern operations.

A Lake that answers back reduces:

• Time to insight
• Time to correlation
• Time to causality
• Time to prediction
• Time to action This is where toil disappears and resilience increases.

How the Lake Answers Back: The Architecture Behind the Behaviour

This article is not about architecture, but it’s important to understand the enabling principles:

1. Index-free design

The Lake reads and reasons directly on data, without slow, expensive indexing.

2. Compute near data

Insights are generated at the source, eliminating latency and infrastructure penalty.

3. Fresh, streaming state

The Lake is always up-to-date. There is no batch. No lag. No caching mismatch.

4. Unified telemetry

Logs, metrics, traces, events — reasoned together.

5. Multi-agent understanding

Keystone decodes behaviour through agents that specialise in change detection, anomaly detection, correlation, and root-cause mapping.

6. Natural-language querying

The barrier between operator and system disappears. Queries become questions. Results become explanations. Put simply: The Lake is designed to understand. Everything else flows from that.

The Business Impact: From Firefighting to Forward

Motion

Proactive observability changes not just operations, but outcomes.

Operational impact:

• Faster MTTR
• Fewer incidents reach users
• Easier on-call rotations
• Fewer escalations
• Less human toil

Financial impact:

• Reduced downtime costs
• Lower infrastructure spend
• No index/storage tax
• Predictable cost model

Customer impact:

• Fewer disruptions
• Better reliability
• Smoother experiences

Strategic impact:

• Resilience becomes a capability
• Engineering becomes more proactive
• Organisations can trust their systems more deeply

This is not about dashboards. It is about the operating model of modern software.

The Future Answers Back — And You Can Experience It Now

This is the beginning of a new era in observability — one where systems don’t just report , they reason.

The Lake marks the transition from:

• dashboards → dialogue
• telemetry → understanding
• queries → explanations
• alerts → predictions
• data → intelligence

This is where observability finally becomes what it should have been all along: a real-time intelligence system that helps you run modern infrastructure with confidence and clarity.

And you can experience it yourself. Here is the link to try Parseable Cloud. Because the future doesn’t just monitor your systems — the future answers back.

This article is originally published on LinkedIn.