Trello is a collaboration tool that organizes projects into boards, each of which can be filled with lists of notes that outline tasks for a team, complete with photos, documents, and other attachments. It includes tools to comment and collaborate among teammates. You can use it as a web-based project management application.

When it was released in 2013, Amazon Redshift was the first cloud data warehouse. It uses defined schemas, columnar data storage, and massively parallel processing (MPP) architecture to provide a base for analytics reporting.

To claim your data from Trello, you can extract it from Trello's servers using the Trello API, a REST API that exposes endpoints that provide information on boards, lists, cards, and actions. For instance, to get data about a list, you might run /lists/[id].

The Trello API returns JSON-formatted data. Here's an example of the kind of response you might see when querying for the details of a list.

[{
    "id": "4efe314cc72846af4e00008a",
    "data": {
        "list": {
            "id": "4eea4ffc91e31d174600004a",
            "name": "To Do Soon"
        },
        "board": {
            "id": "4eea4ffc91e31d1746000046",
            "name": "Example Board"
        },
        "old": {
            "name": "To Do Later"
        }
    },
    "date": "2017-12-30T21:46:52.874Z",
    "idMemberCreator": "4ee7deffe582acdec80000ac",
    "type": "updateList",
    "memberCreator": {
        "id": "4ee7deffe582acdec80000ac",
        "avatarHash": null,
        "fullName": "Joe Tester",
        "initials": "JT",
        "username": "joetester"
    }
}, {
    "id": "4efe3147c72846af4e00006d",
    "data": {
        "list": {
            "id": "4eea4ffc91e31d174600004a",
            "name": "To Do Later"
        },
        "board": {
            "id": "4eea4ffc91e31d1746000046",
            "name": "Example Board"
        },
        "old": {
            "name": "To Do Eventually"
        }
    },
    "date": "2017-12-30T21:46:47.843Z",
    "idMemberCreator": "4ee7deffe582acdec80000ac",
    "type": "updateList",
    "memberCreator": {
        "id": "4ee7deffe582acdec80000ac",
        "avatarHash": null,
        "fullName": "Joe Tester",
        "initials": "JT",
        "username": "joetester"
    }
}]

This part can get tricky: You need to parse the JSON in the API response and map each field to a corresponding table in the destination database. You'll need a solid handle on the datatypes for each endpoint. The Stitch Trello Docs can give you a sense of what datatypes will come through the API.

Once you've identified all the columns you want to insert, you can use the CREATE TABLE statement in Reshift to set up a table to receive your data.

With the table built, you might think that the easiest way to migrate your data (especially if there isn't much of it) would be to build INSERT statements to add data to your Redshift table row by row. Think again! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, we suggest moving the data into Amazon S3 and then using the COPY command to load it into Redshift.

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Trello.

And remember, as with any code, once you write it, you have to maintain it. If Trello modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure SQL Data Warehouse, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To BigQuery, To Postgres, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.