In short. Data Connectors let your team bring ticketing data into StellarAlgo yourselves, instead of waiting on a custom integration. Fast Pass Ticketing is a subject area you set up once: point StellarAlgo at where your ticketing files land, map six standard tables to your own columns, and finalize. Your data then appears at your next scheduled refresh. This capability is turned on per team, so ask your StellarAlgo contact to enable it first.
What you can connect
The Data Connectors screen has three subject areas: Fans, Fan Interactions, and Fast Pass Ticketing. This guide covers Fast Pass Ticketing, which appears only once it is enabled for your team.
You can connect from any of these sources: Amazon S3, Azure Blob Storage, Azure SQL, Google BigQuery, Google Cloud Storage, Snowflake, and Databricks. This guide uses Amazon S3 as an example. The other sources ask for their own connection details.
The six tables you will map
Every Fast Pass Ticketing connector maps the same six tables, in this order: Events, Packages, Tickets, Forwards, Resales, and Scans. These are fixed and do not change from team to team. You tell StellarAlgo which of your files and columns fill each one.
Before you start
Ask your StellarAlgo contact to enable Fast Pass Ticketing for your team.
Have your connection details ready. For Amazon S3 that is the bucket name, the folder prefix where your files land, the Role ARN, and the file type, for example parquet or csv. The bucket can be one StellarAlgo provides or your own, and the access role is arranged with your StellarAlgo team either way: if you drop files into a StellarAlgo bucket, we provide the bucket and an access role; if you use your own bucket, we provide a role that you allow into it. Ask your StellarAlgo contact for these details.
Know which of your files map to each of the six tables above.
Set up the connector
Open Data Connectors, then choose Add Connector.
Choose a subject area. Select Fast Pass Ticketing.
Select a source. Choose where your data lives, for example Amazon S3.
Configure the connection. Give the connector a name. The name is set here and cannot be changed later, so choose it with care. Then enter the connection details. For Amazon S3, the Bucket Name, Prefix, Role ARN, and File Type. You can paste a full S3 URL to fill out the Bucket Name and Prefix for you. Choose Submit configuration.
Your files must be pointed to by a folder prefix that ends in a trailing slash, not a single file. The easiest way is the S3 URL field: paste the full path, for example s3://your-bucket/your-folder/, and it fills in the Bucket Name and Prefix for you. If the path does not end in a slash, you see a warning straight away. If you type the Prefix directly instead, it is not checked for you, so make sure it ends in a slash.
When you submit, StellarAlgo reads the list of tables and columns from your source. This takes a moment. Occasionally this step returns a temporary error saying the schema could not be read. If that happens, try the source configuration again; it usually clears on a second attempt.
Map each of the six tables. For each table, pick the dataset and table from your source, then match your source columns to the StellarAlgo columns. StellarAlgo suggests a match for the required fields automatically. Optional fields are always left blank for you to map by hand. Check the suggestions and fill in anything left empty.
Source System and Ingestion Property are required on every one of the six screens. Use the same values on all six.
Required fields must be mapped. Optional fields are yours to add if you want them and are often used for custom attributes.
If a column type does not match what a field expects, for example a date field mapped to a number, you see a warning. It does not stop you from saving; it is there to catch mistakes, and it only appears for typed fields, not free-text ones.
Finalize. Review the summary of your six tables, then choose Submit. You see a confirmation that your configuration is uploaded, and your data is now syncing.
When will my data appear
Your data is not instant. It appears at your team's next scheduled refresh. Once that refresh starts, allow roughly half an hour to bring the data in and about another half hour for the platform to finish processing, so plan for up to a day depending on your schedule. After that, the new ticketing attributes are ready to use in the Segment Builder.
Editing a connector
The connector name is set when you first configure the source and cannot be changed afterward. To use a different name, delete the connector and create a new one.
Before you finish, you can step back to change the source configuration. You may need to re-enter the connection details when you go back.
You can edit a connector later to finish or change the table mapping.
Connector status
On the Data Connectors list, each connector shows one of these statuses:
Status | What it means | What to do |
Active | The connector is syncing normally or is scheduled for its next sync. Your data is flowing. | Nothing. This is a healthy state. |
Incomplete | The connector was created, but the six-table mapping was never finished. | Open it, complete all six mappings, and finalize. |
Paused | Syncing has been stopped, usually after repeated failed syncs. | Contact StellarAlgo to investigate the failures and resume them. |
Broken | The connection to your source stopped working after previously running, usually because something changed on the source side, such as the access role, the bucket, or its permissions. | Contact StellarAlgo. If you manage the bucket or the role, check if it was not deleted, renamed, or restricted. |
If a connector is both Broken and Incomplete, it shows Broken, because that is the thing to fix first.
If something goes wrong
What you see | What it means | What to do |
A message that the schema could not be read after you submit | A temporary hiccup reading your source | Try the source configuration again. It usually clears on a second attempt. |
A warning on the S3 URL field | The path does not end in a trailing slash | Add the trailing slash so it is read as a folder, for example s3://your-bucket/your-folder/. |
A warning on a mapped field | The column type does not match what the field expects | Check you mapped the right column. You can still save if it is correct. |
My data is not in the platform yet | It appears at the next scheduled refresh, not instantly | Wait for your team's refresh. Allow up to a day. |
FAQ
Which tables do I have to map? All six: Events, Packages, Tickets, Forwards, Resales, and Scans. They are the same for every team.
Can I connect from somewhere other than S3? Yes. Azure Blob Storage, Azure SQL, Google BigQuery, Google Cloud Storage, Snowflake, and Databricks are also supported.
Do I need a Fans connector first? We recommend it.
Who sets this up, my team or StellarAlgo? Yes, it is self-serve. We recommend setting up your first connector together with your StellarAlgo contact.
Can I rename a connector? No. The name is fixed once the connector is finalized. To change it, delete the connector and create a new one.
What's next
These improvements are planned and not yet available.
Enter Source System and Ingestion Property once, up front, instead of on each of the six table screens.
The same setup experience across the Fans, Fan Interactions, and Fast Pass Ticketing connectors.
Move between the setup steps in any order, rather than only in sequence.
When you step back to the source configuration, your non-sensitive entries such as the bucket name and prefix are kept. Sensitive details are never stored.
Still need help?
Contact StellarAlgo support@stellaralgo.com, or your StellarAlgo contact, with your connector name.