Dataspaces
What is a dataspace?
A dataspace is a logical container for one or more datasets. It defines shared configurations and access boundaries across those datasets.
Each dataspace acts as a control layer for:
- Routing logic
- Storage structure
- Retention policies
- Access control
- Schema enforcement
You query datasets with DataPrime inside a dataspace using:
Note
If you're working in the default
dataspace (which most users do by default), you can omit the prefix entirely.
Why use dataspaces?
Dataspaces make it easier to manage observability data at scale, especially when you need:
- Policy consistency: Apply routing, retention, and enrichment at the group level.
- Simplified access control: Grant users access to an entire dataspace rather than individual datasets.
- Forward-compatible design: Automatically extend policies to new datasets as they are created.
How configuration inheritance works
When a dataset is created inside a dataspace, it automatically inherits the dataspace's configuration, such as:
- S3 storage paths
- Retention rules
- Access policies
- Metadata enrichment
For example, if a dataspace defines this S3 path:
Then new datasets inside that dataspace will automatically write to:
s3://my-bucket/my_prefix/dataset1
s3://my-bucket/my_prefix/dataset2
s3://my-bucket/my_prefix/dataset3
This inheritance is dynamic — no manual setup is needed when new datasets appear.
Data flow with dataspaces
The end-to-end journey looks like this:
Ingestion
Logs, metrics, spans, and events are ingested into Coralogix.
Routing
The TrafficProcessor applies routing rules to determine the appropriate dataset and dataspaces. For example, routing events to
default.{{$d.region}}-events
will create a separate dataset for each region encountered in the data. Data is distributed across multiple destinations accordingly.Dataset creation
New datasets are created automatically inside the target dataspace.
Configuration application
Each dataset inherits settings from the parent dataspace including:
- Storage destination
- Retention policies
- Access controls
- Enrichment rules
Final storage and access
Data is stored according to blueprint paths (e.g.,
s3://bucket/prefix/dataset-name
) and made available for search, visualization, and alerting.
Types of dataspaces
Coralogix supports three kinds of dataspaces:
Type | Description |
---|---|
default | The main user-facing dataspace. Contains DataSets like logs , spans , and others unless specified. |
system | A Coralogix-managed dataspace for internal datasets such as alert history, audit events, and schema metadata. |
user-defined | forthcoming feature: Custom dataspaces created by users to segment data by team, region, environment, or use case. |
The default
dataspace
All user data flows into default
unless routed elsewhere. Common datasets include:
Shortcut behavior:
If you don’t specify a dataspace, default/
is assumed.
These are all equivalent:
If you're only using default
, there's no need to change your query syntax.
The system
dataspace
The system
dataspace contains internal datasets populated and maintained by Coralogix. These include:
alerts.history
– triggered alerts and metadataengine.queries
– history of alert eventsengine.schema_fields
– snapshots of schema evolutionnotification.deliveries
– notification trackingnotification.requests
– notification tracking
The data is read-only and structured for analysis via Explore and Custom Dashboards.