Azure Event Hubs
by Microsoft
Azure Event Hubs ingests telemetry and application events through AMQP or Kafka endpoints, spread across partitions and read by consumer groups. A Python connector reads those events and lands them in the Lake in scheduled batches with partition and offset kept. Short retention windows stop mattering once the events are stored and joined with business data.
- Category
- Cloud & Data Platforms
- Type
- Streaming & messaging
- Interface
- SDK
- Built with
- Connect Anything
- Credentials
- OAuth 2.0, Vendor credentials
02Data
What you can sync.
- Events
- Partitions
- Consumer groups
- Event hub metadata
Load Azure Event Hubs events into your Lake and model it in the Warehouse with the rest of your stack.
Need more? Name any other Azure Event Hubs data the API returns, and the AI adds it to the connector.
03Credentials
Credentials you will need.
- OAuth 2.0 (client ID, client secret, refresh token)
- Other credentials
Azure Event Hubs uses OAuth 2.0 (client ID, client secret, refresh token) or other credentials. You add the credentials once, when the AI asks for them, and it tells you where to create them in Microsoft if you need help.
04Questions
Questions you can answer.
Once Azure Event Hubs data is in your Lake, ask an AI Mind in plain words or build the answer in DataPrism BI.
- How many telemetry events per device did each site send yesterday, and which devices went silent?
- Which partitions receive a disproportionate share of events?
- How do hourly event counts compare before and after the last deployment?
- Do application error events cluster around particular customers or regions?
05How it works
How it works in DataMind.
-
Pick Azure Event Hubs.
Open Lake, pick Azure Event Hubs in the source picker and name the connection. Connect Anything opens with a ready prompt that points the AI at the Azure Event Hubs API docs.
-
Answer a few questions.
The AI reads the Azure Event Hubs documentation, then asks which credentials you will use, which data to sync (for example events and partitions), whether you want a one-time load or a recurring sync, and which accounts or filters apply.
-
Your data lands in Lake.
The connector agent writes a Python connector for Azure Event Hubs, tests it against the live API and shows you sample rows. Then the data loads into your Lake on your schedule, ready for the Warehouse, DataPrism BI and AI Minds.
06Pairs
Works well with Azure Event Hubs.
Sources teams join with Azure Event Hubs in the Warehouse, each one connected the same way.
07Related
