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Public preview Maia is designed to simplify complex and resource-intensive platform migration projects. Its integrated capabilities help accelerate the conversion of data workloads from existing platforms into optimized, cloud-native pipelines.

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Video example

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Prerequisites

Before you begin, ensure the following requirements are met:
  • Export project files from the source platform and store them locally or in an accessible Git repository (such as GitHub or GitLab). For more information on integrating your Git account with , read Connect your own Git repo.
  • An active account is available.
  • Maia is enabled and accessible in your environment. Read Edit account details to learn how to enable Maia if it isn’t enabled for your account.
  • Your role includes the required permissions to create and deploy pipelines in the target project and environment. For more information on account roles, read Role based Access Control overview.
    • Maia supports a growing set of data workload conversions from major data platforms, with support for additional platforms such as Ab Initio coming soon.
    • If you need to convert from a platform that isn’t currently supported, contact your account team to discuss your requirements.

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Supported file types for data workload conversions

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Convert workloads with Maia

  1. Log in to and select the project you want to use.
  2. Click the Files icon, select Add (+) from the dropdown menu, and then select Convert workloads.
  3. In the Convert workloads with Maia dialog , select a workload type. All supported platforms are listed under Workload type.
  4. Choose a file loading method: Git repository files or Upload files.
    • If you choose Git repository files, make sure all workload files are already available in your Git repository, use the search feature to select the files you want Maia to convert, and ensure that each file is 10 MB or smaller.
    • If you choose Upload files, you can upload up to 10 files at a time, provided the combined size of all selected files does not exceed 20 MB.
  5. (Optional) Conversions are often more successful when additional context is provided. For guidance on what to include, read Best practices for workload conversion. To add context, edit the conversion-specific context file (conversions/user-guidance.md) displayed on screen. This file can include pipeline design standards, schema information, or any other details relevant to the conversion. Maia will also use any other context files that have already been created.
  6. Click Submit to begin the analysis. Maia analyzes the workload files. The analysis time varies based on file size.
  7. After the analysis is complete, choose how you want to proceed:
    • Plan conversion with Maia: Work with Maia to create and refine a conversion plan. Once you’re happy, Maia will execute the agreed plan.
    • Begin conversion without planning: Maia will begin building pipeline(s) immediately without further planning and without asking for approval. Choose this option when you have high confidence that Maia will deliver the expected outcome without additional guidance.
  8. Click Submit.

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Validate Maia pipeline outputs

When you use Maia to create data pipelines in a environment, validation helps ensure that the output from the newly created pipelines is consistent and comparable with the output from the source platform workloads.

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Load source outputs into the cloud data warehouse

If the output from the source platform workloads is not already in the same cloud data warehouse used by , load it into that cloud data warehouse to simplify comparison. Load the output into a different schema within the same database as the output. Files such as .txt, .csv, or .xlsx can be saved to cloud storage and loaded using a orchestration pipeline. Using fixed input data makes side-by-side validation easier.
You can choose not to load the output into the cloud data warehouse; however, this approach requires more manual validation and is generally less efficient.

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Compare pipeline outputs

Follow these steps to compare the output of a pipeline with the output from another product.
  1. Ensure that the output from both and the other product is available in the same cloud data warehouse.
  2. Create a new transformation pipeline in and add the Table Input component.
  3. Use the Table Input component to load the output tables from both and the other product.
  4. Add components such as Detect Changes, Assert View, or Aggregate to compare the results.
You can also ask Maia to compare the two tables and review the results.

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Troubleshoot workload conversion errors

When converting workloads from other platforms to pipelines, you may encounter the following error messages. Review the advice column for each message to resolve the issue.