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The Datadog data connector creates read-only snapshots of selected Datadog queries as typed Summation tables. Use them in reports, dashboards, and joins with your other data. No raw JSON table or follow-up calculation table is required.
This connector is available to enabled workspaces during rollout. If Datadog is not listed under data sources, contact your Summation administrator.

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What you’ll need

  • Your organization’s Datadog site, such as datadoghq.com, us3.datadoghq.com, or datadoghq.eu.
  • An API key and an application key for the same Datadog organization.
  • Read permissions for each dataset you plan to import.
In Datadog, open Organization Settings, then API Keys and Application Keys to create or obtain the keys. Prefer a dedicated service account and scope its application key to the permissions below. The key owner must also have those permissions. See Datadog’s key management instructions. Summation stores both keys as connector secrets. Do not enter keys in dataset names or queries. This connector does not request write permissions or use an OAuth sign-in flow.

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Connect Datadog

  1. Open Connectors, select New connection, and choose Datadog.
  2. Enter a connection name and select the Datadog site that matches your organization.
  3. Enter the API key and Application key.
  4. Optionally enable Scheduled snapshots and choose a cadence.
  5. Select Test connection, then Continue.
  6. Select Add Dataset. Choose a dataset first; Summation fills in its default name. You can rename it before saving.
  7. Set the query and, where available, time range. Add other datasets as needed, then select Create connector.
The initial import starts automatically. Tables become available when their first snapshots finish. A successful connection test validates the key pair, not access to every dataset; the first import also checks that dataset’s permissions.

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Choose a dataset

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Logs

Enter a Datadog log search query, such as env:production service:checkout status:error. Choose a narrow time range to start. The table contains the listed common fields, not every custom log attribute.

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Metrics

Enter a metric query, for example avg:system.cpu.user{env:production} by {host}. The table contains the points returned by Datadog, with their series, scope, and unit. This is not a download of every metric in your organization.

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Monitors

Enter a monitor search query, such as type:metric status:alert, or leave it blank for accessible monitors. This dataset is current monitor inventory, not alert history, so it has no time-range control.

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Log aggregates

Enter a log search query and select Count, Sum, Average, Minimum, Maximum, or 95th percentile. Other than Count, these aggregations require a numeric Measure, such as @duration, available in your logs. Optionally group by a facet such as service, and choose an hourly or daily bucket interval. For example, count env:production status:error by service over the last 24 hours with hourly buckets to report error volume without importing individual events.

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Time ranges and refreshes

Logs, Metrics, and Log aggregates support Last hour, Last 24 hours, Last 7 days, Last 30 days, or a Custom range. Custom From and To values are in UTC and must define a positive range of at most 31 days.
  • Relative ranges are evaluated when a refresh starts. Retries retain that refresh’s time window.
  • Custom ranges stay fixed until you edit them. Repeating a refresh does not advance the dates.
  • Every successful refresh replaces the table’s contents with the selected query result. Incremental append is not supported.
  • If a refresh fails, the previously materialized table remains available. A successful query with no matches produces an empty table.
  • Use the dataset’s refresh control for a manual update, or enable Scheduled snapshots for recurring updates.
A daily refresh of Last 24 hours maintains a rolling window, not a growing historical archive. Data outside that window is removed from the current table after a successful refresh. Availability also depends on Datadog retention and the connected account’s access.
Start with a narrow query and a short window. Large exports or Datadog throttling can make refreshes take longer; avoid scheduling frequent full exports of high-volume logs. Prefer log aggregates when you only need counts or trends.

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Snapshots or MCP?

Use this data connector for repeatable reporting and cross-source analysis on a defined subset of telemetry. Use an app or MCP connection with appropriate live-query tools for occasional investigations where you only read a small fraction of the source data. You do not need to mirror all Datadog data into Summation. Raw APM trace/span search and service dependency exploration are not datasets in this version of the data connector. They require a separate supported integration; the four datasets above are the current scope.

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Common problems