Using Hop to transform data and write to another database

[HaruspexSan]

Hello Apache-Hop Community,

Disclaimer: I am new to Apache Hop and using it for my Master's thesis. I appreciate any guidance!

The Goal: I am building a pipeline to populate a BI fact table from a production Postgres database.

Source: A Users table containing raw transactional data (Granularity: 1 row per user).
Fields: ID, created_at, deleted_at, status etc.

Target: A Daily_Stats BI table (Granularity: 1 row per day).
Desired Fields: Date, NewUsersThatDay, TotalUserCount (Cumulative).

The Problem: I am struggling to transform the row-level user data into the aggregated daily format within a single pipeline.
Especially with the issue of different numbers of input fields per stream part.

Furthermore, users with the deleted_at field not being null be deleted after 30 days. Which means I need to add the functionality that after the status changes from active to deleted, i need to reduce the running count by 1 as the user would otherwise be counted in the "rolling count" eventhough it was deleted.

So my questions are:
Is all of this possible in one big pipeline? Or do I have to split it up into several?
How do i use scripting or SQL or any other transform to calculate the rolling count, especially because i need the last rolling count as input from the database and table i will be writing my output to?
How do i calculate the NewUsersThatDay field as i have to aggregate not within a row but with the rows from that day?

Thank you so much in advance

[bamaer]

@harrisward created a sample repository that loads data from the MySQL Sakila database to a small data warehouse, you'll find some great examples there.

To aggregate: Group By transform. This requires sorted data. For small data sets that fit entirely in memory, you can use the Memory Group By transform.

For the deleted users: either exclude those users from your input query or with a Filter Rows transform.

Your questions:

• A best practice is to split your work in smaller manageable pipelines instead of fewer large/huge pipelines.
• SQL: Table Input should get you a (very) long way
• Scripting: Script, Javascript transforms. Scripting is very much possible, but try to use it as a last resort
• NewUserThatDay: something like Table/File Input -> (optional) Filter to exclude deleted -> Group By ("that day" would be your key, number of rows as type, user_id as subject).