IBM MQ
by IBM
Enterprise message queuing middleware; the messaging REST API reads messages from queues and the administrative REST API returns queue status.
- Category
- Cloud & Data Platforms
- Type
- Streaming & messaging
- Interface
- REST API
- Built with
- Connect Any API
- Credentials
- Username and password, Vendor credentials
- Docs
- IBM MQ API docs
02Data
What you can sync.
- Queue messages
- Queues
- Queue managers
- Channel status
Load IBM MQ queue messages into your Lake and model it in the Warehouse with the rest of your stack.
Need more? Name any other IBM MQ data the API returns, and the AI adds it to the connector.
03Credentials
Credentials you will need.
- A username and password
- Other credentials
IBM MQ uses a username and password or other credentials. You add the credentials once, when the AI asks for them, and it tells you where to create them in IBM if you need help.
04How it works
How it works in DataMind.
-
Pick IBM MQ.
Open Lake, pick IBM MQ in the source picker and name the connection. Connect Any API opens with a ready prompt that points the AI at the IBM MQ API docs.
-
Answer a few questions.
The AI reads the IBM MQ documentation, then asks which credentials you will use, which data to sync (for example queue messages and queues), whether you want a one-time load or a recurring sync, and which accounts or filters apply.
-
Your data lands in Lake.
It builds the connector, tests it and shows you sample rows. Then IBM MQ data loads into your Lake on your schedule, ready for the Warehouse, DataPrism BI and AI Minds.
05Related
