Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.maia.ai/llms.txt

Use this file to discover all available pages before exploring further.

The is a component within that serves as a bridge between the source database and the target cloud data lake, enabling the execution and scheduling of streaming pipelines. The agent will be hosted in your own infrastructure, using a Hybrid SaaS solution. Once the is configured and started, it operates autonomously without requiring much intervention. The agent continuously monitors all changes occurring in the source database, consumes those changes from the low-level logs, and delivers them to the designated target data lake or storage. This ensures a continuous and reliable change data capture process. Create a in your own infrastructure. You can create agents in the user interface or provision them programmatically when deploying streaming environments at scale. We currently support agents running in AWS, Azure, and GCP infrastructure.
Each can run only one Streaming pipeline. Each Streaming pipeline requires a new agent installation.

​
Prerequisites

  • A account. To register, read Registration. Once you have signed up, log in to .
  • An account in AWS, Azure, or GCP to host the agent.
  • Access to a cloud secrets service, which is a secure storage system for storing authentication and connection secrets. These secrets are used by the agent to authenticate itself with the source database and establish a secure connection for capturing the data changes.
  • Allowlisted IP addresses configured for your environment. For more information, read Hybrid SaaS agents and Git repositories.
Your source database will also require configuration to work with Streaming pipelines. This is independent from the agent installation process. More information can be found in the documentation for each supported data source.

​
Create an agent

  1. In the left navigation, click s. Then, select Runners from the menu.
  2. All currently created agents are listed showing their Status, Platform (AWS, Azure, or GCP), and Type ( or Streaming).
  3. Click Add runner.
  4. Click Streaming.
  5. Complete the following properties:
    • Runner name: A unique name for your new agent. Maximum 30 characters. Accepts both uppercase and lowercase A-z, 0-9, whitespace (not the first character), hyphens and underscores.
    • Description: Optionally enter a brief description of the agent.
    • Cloud provider: The cloud platform that the agent will be deployed to. Currently, AWS, Azure, and GCP are supported.
    • Deployment: The supported deployment method for the given cloud provider. Currently, Fargate and EKS for AWS, ACI and AKS for Azure, and GKE and GCE for GCP are supported.
  6. Click Create runner.
This creates an agent definition in , and displays the agent’s parameters on the Runner details screen. The agent’s status is set to Pending, which means it is not yet ready to run pipelines. The next step is to deploy the agent application into your cloud infrastructure, as described below.

​
Set up the agent in your cloud infrastructure

After creating the agent in with the above process, the agent needs to be installed into your cloud infrastructure. There are several different ways of doing this, and you can use whichever method suits you: To complete these processes, you will require certain details of the created agent. To obtain these details, locate the agent in the list of agents, and click the three dots … next to it, then click Runner details. The parameters and values in the sections Agent image URI, Agent environment variables, and Credentials are required when configuring your agent in your cloud infrastructure. You can also use Terraform to deploy s and pipelines across AWS, Azure, and GCP. For more information, see Deploy with Terraform.

​
Check agent status

After deploying the agent in your cloud infrastructure, you should return to to verify that it’s correctly connected and running.
  1. Click the menu button in the top left of any screen, then click Manage runners.
  2. Locate the agent in the list and check the status:
    • Pending: The agent has been created but has not yet connected to .
    • Running: The agent is connected and available for running Streaming pipelines, or is connected and already running a Streaming pipeline.
    • Stopped: The agent has been stopped.
    • Unknown: The agent is in an unknown state. The typically means the agent has lost connection to without being stopped, for example due to networking issues.
  3. When the agent status shows Running, it’s ready to use. It can be selected in the Runner drop-down when you create a new Streaming pipeline, as long as a pipeline is not already assigned.

​
Deleting agents

To delete an agent from , locate the agent in the Runners list, then click the three dots … → Remove runner. This action is irreversible, so be sure that you want to continue. Deleting the agent from the Runners list doesn’t remove the underlying AWS, Azure, or GCP resources. You should go into the AWS Console, Azure Portal, or GCP Console and clean up any resources that you no longer require.
Deleting an agent that is currently running may interrupt pipelines that are currently running. Therefore, you should always stop the agent service before deleting it.