Slack connector with in MULE

1. Slack connector in MULE ESB

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Slack connector in MULE ESB
Slack connector provides 2-way integration between slack messaging
system to others in MULE ESB
Slack - cloud based product which enables real time messaging, archiving,
search & more (more like chat and messenger app).
Refer for more information - https://www.slack.com
MULE ESB is integration framework from MULESOFT which has both
service container and mediation framework.
Developed on java platform and not limiting to point to point integration

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Slack connector in MULE ESB-example
Here I will show case the message streaming from Slack using thier api in MULE ESB
Slack connector need to install separately from Studio , please follow below instructions to do
the installation.
•Click on Help->InstallNewSoftware
•Select the repository for the connectors
(http://repository.mulesoft.org/connectors/releases/3.5.0)

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Slack connector in MULE ESB-example
• Select the slack connector, follow the instructions and install it.
• Restart Anypoint Studio to reflect the changes
• You will be able to see the slack connector symbol in the Mule palette.

5. 5
Slack connector in MULE ESB-example
First create flow element then drag the slack component into the flow.
Configure the slack connector configuration, used token configuration in the below example.
MULE provides integration using token OR OAUTH (Please refer the slack api guide more
information for generating api token)

6. 6
Slack connector in MULE ESB-example

7. 7
Slack connector in MULE ESB-example
Configure the operation need in the slack connector(in this example, we are retrieving the
message from slack).

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Slack connector in MULE ESB-example
Read the message from Slack and display in the logger, to do that drag and drop the logger
component in the flow.
Note: Some times you will get the error during deployment saying slack connector schema is
not exist and the solution is add the slack connector jar in the build path.
Tip - You can build your own conversation bot using slack, API.AI for NLP processing and this
can be achieved in MULE using http connector and MULE object store, here integration is
backbone and MULE connectors can help you.