Dynatrace
Dynatrace maps the hosts, processes, services and dependencies it discovers, then opens a problem whenever its Davis engine sees an anomaly. Pulling those problems, entity records, metric series and audit logs into your Lake gives them business context the console lacks: which customers sat behind a degraded service, what a slow week cost in orders, and which teams own the entities that break most often.
- Category
- IT, Security & Monitoring
- Type
- APM & observability
- Interface
- REST API
- Built with
- Connect Any API
- Credentials
- API key, OAuth 2.0
02Data
What you can sync.
- Metrics
- Problems
- Entities
- Logs
- Audit logs
Join Dynatrace metrics with assets and people data to see risk by team, system and owner.
Need more? Name any other Dynatrace data the API returns, and the AI adds it to the connector.
03Credentials
Credentials you will need.
- An API key
- OAuth 2.0 (client ID, client secret, refresh token)
Dynatrace uses an API key or OAuth 2.0 (client ID, client secret, refresh token). You add the credentials once, when the AI asks for them, and it tells you where to create them in Dynatrace if you need help.
04Questions
Questions you can answer.
Once Dynatrace data is in your Lake, ask an AI Mind in plain words or build the answer in DataPrism BI.
- Which services opened the most Davis problems last quarter, and how long did each stay open?
- What share of monitored entities carry no owner tag, and which management zones hold them?
- Do problems on checkout services line up with dips in completed orders for the same hour?
- Which audit log entries show configuration changes made in the hour before a problem opened?
- Which hosts with sustained high CPU metrics also produce the most error log lines?
05How it works
How it works in DataMind.
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Pick Dynatrace.
Open Lake, pick Dynatrace in the source picker and name the connection. Connect Any API opens with a ready prompt that points the AI at the Dynatrace API docs.
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Answer a few questions.
The AI reads the Dynatrace documentation, then asks which credentials you will use, which data to sync (for example metrics and problems), whether you want a one-time load or a recurring sync, and which accounts or filters apply.
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Your data lands in Lake.
It builds the connector, tests it and shows you sample rows. Then Dynatrace data loads into your Lake on your schedule, ready for the Warehouse, DataPrism BI and AI Minds.
06Pairs
Works well with Dynatrace.
Sources teams join with Dynatrace in the Warehouse, each one connected the same way.
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PagerDuty Which Dynatrace problems became PagerDuty incidents, and how long did each take to acknowledge and resolve? -
GitHub Which deployments or merged pull requests came just before a rise in problems on the same service? -
ServiceNow Which ServiceNow incidents trace back to a Dynatrace problem, and which configuration items keep recurring?
