Codespaces Meltano DIY Demo

Welcome to Meltano! Run your first data pipeline within 5 minutes.

Even if you never touched Meltano before. No install needed, just a GitHub account (and a few spare Codespaces minutes you get for free anyways).

Let's get started!

Open codespaces (if it isn't already open)

Click "Open on Codespaces", to launch this project into a ready to use web VS-Code version with everything preloaded.

Make sure to open up the README.md inside Codespaces as well.

Notes on codespaces:

- If you at any point get an error "The user denied permission to use Service Worker", then you need to enable third-party cookies. It's a codespaces related problem.

- In our experience, codespaces work best in Chrome or Firefox, not so well in Safari.

- Files in codespaces autosave! No need to save anything.

What you're building: let's run the final data pipeline first.

There's a csv customers.csv with

customer names, e-mail adresses and IPs
you're going to extract this CSV and load it into an SQL-database.

Go ahead, just run

meltano run tap-csv hide-ips target-duckdb

And that's it, you're done. Don't believe us? You can use a helper function to check the SQL-database:

./meltano_tut select_db

Watch out for these things:

There are no ip addresses inside the database, right? Check customers.csv, they were there.
That's because we added a "mapper" called "hide-ips" that is completely customizable and in this case hashes the IP addresses.
In the console output - Meltano told you at the beginning of the log ... "Schema 'raw' does not exist."
That is because Meltano has a lot of helper functions. It e.g. creates schemas and tables, should they not already exist.

Feel free to explore the project, or dive right into building it yourself!

Let's go ahead and build it ourselves within 5 minutes

Step 1 - initialize a new meltano project

Inside the terminal (bottom window) run:

./meltano_tut init

This runs a wrapped "meltano init", adding demo data for you to have fun with. This will remove what we preinstalled, so now we need to add a few things first.

Step 2 - add your first extractor

Add your first extractor to get data from the CSV. Do so by running inside the terminal:

meltano add extractor tap-csv

Then open up the file meltano.yml, copy the config below, and paste it below pip_url.

    config:
      files:
      - entity: raw_customers
        path: data/customers.csv
        keys: [id]

Your complete config for tap-csv in meltano.yml should look like this:

plugins:
  extractors:
  - name: tap-csv
    variant: meltanolabs
    pip_url: git+https://github.com/MeltanoLabs/tap-csv.git
    config:
      files:
      - entity: raw_customers
        path: data/customers.csv
        keys: [id]

Step 3 - test run your tap

Let's test the tap by running:

meltano invoke tap-csv

If everything works as expected, Meltano should extract the CSV and dump it as a "stream" onto standard output inside the terminal.

Step 4 - add a loader

Next add a loader to load our data into a local duckdb:

meltano add loader target-duckdb

Copy the configuration below and paste it below the pip_url for target-duckdb in the meltano.yml file.

    config:
      filepath: output/my.duckdb
      default_target_schema: raw

The config in meltano.yml for target-duckdb should look like this:

  loaders:
  - name: target-duckdb
    variant: jwills
    pip_url: target-duckdb~=0.4
    config:
      filepath: output/my.duckdb
      default_target_schema: raw

Step 5 - run your EL pipeline

Now you can do your first complete EL run by calling meltano run!

meltano run tap-csv target-duckdb

Perfect!

Step 6 - view loaded data

To view your data you can use our little helper: