Maia Workshops | Getting Started

Maia Workshops | Getting Started

Getting Started with Your Maia Workshop

Welcome! You're about to experience something a bit different from a typical hands-on workshop. Instead of following a one-size-fits-all script, you'll be working with Maia — an AI assistant that will create a personalized workshop just for you, based on your role, industry, and experience level.

No prior knowledge of Matillion or data pipelines is needed. Maia will guide you through everything.

Start with Step 1
1
Step 1

Log In to the Workshop Environment

  1. Open your browser and go to https://app.matillion.com
  2. Enter your credentials:
    Username:
    <prefix>+<number>@matillion.com — your facilitator will tell you the prefix and number to use for this session (e.g. maiaworkshop+101@matillion.com or aidatagames+3@matillion.com)
    Password:
    Provided by your facilitator at the start of the session
    Maia log in screen with the email and password fields highlighted
  3. Once logged in, click the "Design Data Pipelines" tile in the top-left area of the screen
    Services panel showing the Design data pipelines tile
2
Step 2

Find Your Project and Branch

  1. You'll see a list of projects — select the one available
  2. Inside the project, select the branch that matches your assigned number (e.g., Maia Workshop 8)

Think of a branch as your own private workspace — anything you build here won't affect anyone else.

3
Step 3

Start the Workshop

Here's where things get interesting. Rather than handing you a static set of instructions, Maia will build a custom workshop tailored to you.

  1. Click the Maia icon (top-left corner in the canvas) to open a chat
    Maia icon in the top-left corner of the canvas, highlighted with an arrow
  2. Type: "start a workshop" and press <enter>
    Maia chat panel with the start a workshop prompt typed into the input box
  3. Maia will ask you a few quick questions:
    • Your name
    • What you do day-to-day (e.g., marketing, finance, operations)
    • How comfortable you are with data tools
    • Whether you'd like an optional migration exercise (feel free to skip if it's not relevant)
  4. Based on your answers, Maia will suggest sample data tables relevant to your role and industry. Review these and confirm — or ask Maia to adjust them
  5. Maia will then spend ~3–5 minutes generating your data and building a personalized workshop guide

During setup, Maia will ask for permission to run pipelines and sample data. Choose Accept each time to let Maia finish building your environment.

4
Step 4

Working Through Exercises

Once setup is complete, your Workshop Walkthrough Guide will appear on the right side of the screen.

If the guide looks like raw code, click the Preview toggle at the top to switch to a readable view.

Code, Split, Preview toggle buttons with Preview highlighted

The guide contains several exercises. Here's how the workflow goes:

  1. Read the exercise in the guide (start with Exercise 1)
  2. Copy the prompt from the guide and paste it into the Maia chat
  3. Watch Maia work — it will build a pipeline, explain what it did, and optionally walk you through the result in the UI
  4. Return to the guide to find your next exercise (use the Workshop Walkthrough tab at the top of the screen)

Tips for getting the most out of it:

  • Don't just rush through prompts. Take a moment to look at what Maia built and understand the pipeline visually.
  • Try the "Deep Dive" ideas included in each exercise — these help you explore the platform beyond the basics.
  • New tabs will appear as Maia creates pipelines. You can always navigate back to your guide using the tabs at the top.
5
Step 5

Wrapping Up and Providing Feedback

When you've completed the exercises (or as many as you'd like):

  • Maia will suggest providing feedback on your experience
  • You can also give feedback at any time by simply telling Maia — for example: "I'd like to provide feedback"

Your feedback helps us improve future workshops, so we'd really appreciate hearing from you!

That's it — enjoy the workshop! If you get stuck at any point, just ask Maia (or your workshop facilitator). We’re here to help. 🎉

Appendix

Legacy Pipeline Migration Exercise

Goal:
Convert a legacy pipeline export into a visual Maia pipeline using the Convert Workloads feature.

What's happening: During workshop setup, Maia generated a sample legacy tool export file in your project (located in the Workshop/ folder). This file replicates the same transformation logic you built in Exercise 1 — but expressed in the verbose, tool-specific format of your chosen legacy platform. You'll use Maia's built-in conversion tool to turn it into a visual pipeline automatically.

Step 1 — Generate the migration file (if deferred)

If you chose to defer migration file generation during setup, ask Maia to create it now:

"Generate the sample migration file for the conversion exercise"

Once Maia confirms the file is ready, proceed to Step 2. If your file was already generated during setup, skip directly to Step 2.

Step 2 — Convert the workload

  1. On the top left of the canvas, click on the down caret just to the right of Files
  2. Select Convert Workloads
  3. Set Workload Type to match the legacy tool you selected during setup (e.g., Informatica PowerCenter, IBM DataStage, SSIS, dbt, etc.)
  4. Set File loading method to Git repository file
  5. Click Continue
  6. In the Select Files dialog, enter the path to your migration file: Workshop/<your-migration-file>
    • If you're unsure of the filename, check the Workshop/ folder in the Files browser on the left panel
  7. Click Convert and wait for Maia to analyze and generate the pipeline
  8. Review the converted pipeline — walk through the components to see how the verbose legacy definition was translated into clean visual components
  9. Click Accept to add it to your project
  10. Open the pipeline and sample a few components to verify the output

Expected output

A Maia transformation pipeline with visual components that replaces the entire legacy tool definition. Notice how hundreds of lines of tool-specific XML/JSON/SQL configuration become a handful of clearly-named, visual components.

Deep Dive Ideas (try these if you finish early)

  • Compare the converted pipeline side-by-side with your Exercise 1 pipeline — are the results identical?
  • Try modifying the converted pipeline (add a filter, rename a column) to see how easy it is to iterate on migrated work
  • Ask Maia to document the converted pipeline with canvas notes explaining what each component does