Data governance

Dataspaces and Datasets

A structured data layer that gives teams and agents control over how observability is organized, routed, and governed.

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From data chaos to a faster, governed, structured data layer

clock

Dashboards that stay fast as you scale

Tighter scopes and aggregated data cut query times and keep them consistent regardless of data volume.

inventory

Team independence without account sprawl

Give each team a governed dataset with isolated schemas, scoped access, and independent retention, no extra accounts needed.

budget

Know exactly what your data costs

Track per-dataset ingestion with trends and quotas. Instantly answer cost investigations without a spreadsheet.

Create your own datasets for any observability use-case

User-defined datasets let you route your telemetry into governed, named containers that mirror your operations, all inside a single Coralogix account.

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AI-native by design

Scoped, contextual data means agents reason precisely on fewer tokens instead of guessing.

How your data is organized

• A Dataspace is a logical container for organizing data and applying policies.
• A Dataset is a governed collection inside it, with its own schema, access, retention, and cost tracking.

Data lands in the default dataspace. Route data to user made dataset, segment data by team, service, or any use-case; use the system Dataspace to query platform telemetry like alerts, schema health, audits, and more.

Performance

Scoped data, faster everything

Scoped datasets mean faster queries and lighter dashboards. Streaming datasets isolate noisy data. Summary datasets turn heavy queries into persistent assets that load instantly.

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Governance

Shape data to match your org

User-defined named datasets that reflect your org structure. Each is self-describing with its own schema, access, and retention. System datasets surface platform health, audits, and usage automatically.

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Routing

Route by any field, any logic

Route any field with granular DataPrime expressions in TCO, or programmatically with writeTo. Fan out logs to multiple datasets when compliance and operations need different views.

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Cost control

Know what every team costs

Per-dataset attribution with daily breakdowns and enforceable quotas. Know exactly what each team generates. Scoped data also means fewer tokens per AI query.

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AI-native

Architecture that makes agents smarter

Scoped context means agents reason precisely instead of guessing. Clean schemas reduce hallucination. Governed access controls what agents reach. Pre-aggregated data cuts token cost.

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One architecture. Many uses

Streaming Datasets

Give every team its own governed data lane
Create named datasets that match the way your org works - by team, service, or any use-case. Route data in with granular DataPrime expressions or programmatically with writeTo. Each dataset gets its own schema, access, and retention.

- Route by any field, any condition
- Fan-out to multiple governed datasets
- Self-describing, collision-free schemas per team

Summary Datasets

Run the heavy query once. Dashboards stay instant.
Automatically write background query results into persistent datasets. Reuse them in Explore, Dashboards, and APIs indefinitely. Terabytes of raw logs become megabytes of summary. Multiple queries can write to the same dataset, compounding analytical value.

- Persistent results, queryable anywhere
- Append or overwrite with versioning
- Splunk summary index replacement, native

System Datasets

Your platform's behavior as queryable data
The system dataspace tracks platform activity with datasets that cover query performance, schema health, alerts, usage, audit trails, and more - all queryable with DataPrime. Agents can reason about the platform itself, not just your applications.

- 7+ live datasets, queryable today
- Query Performance Dashboard built-in
- Platform intelligence for agents and admins