Bringing Data Together
Knowing the best path for working with multiple datasets can be confusing sometimes, understanding which approach is appropriate for what your wanting to achieve is key to getting the outcome you want.
Here's a straight forward breakdown of 4 out-of-the-box Aperture Data Studio operations which will allow you to bring your data family together.
👬Union - Combining Multiple Inputs into a Single (Guest)List
Imagine you and your partner are hosting a dinner party where you will be introducing your families for the first time - stressful I know. You each make a list of who you want to invite and then combine this list to create the group chat titled 'Relative Chaos' or something equally embarrassing.
In Data Terms: A Union stacks our datasets vertically and is used when we have two datasets with similar structures that we want to treat as one.
Why use a Union?
✅ Useful for consolidating data from different sources
✅ Creates a complete picture without losing any records
🔍 Lookup - Enrichment or Finding Unexpected Connections
At the dinner, your sister is chatting with your partners cousin and mentions some of their favourite hobbies like yoga, hiking and a strange love for Will Ferrell Movies only to find out they have all of those things in common - Did we just become best friends?? They spent the next 20 minutes looking through IMDB discussing which movie of his was best.
In Data Terms: A Lookup pulls in additional information from a reference dataset to return corresponding values. Think of it as enriching a record with more context.
Why use a Lookup?
✅ Enriches data with additional context
✅ Helps uncover meaningful relationships
🤝Join - Connecting on a Greater Level
They move on to yoga, and now things get weirder, turns out they are members at the same Studio. They start making plans of attending a session together next week.
In Data Terms: A Join links two datasets based on a shared column - like a customer ID, email or similar. Linking related records across tables.
Why use a Join?
✅ Enables deeper analysis by combining related data
✅ Essential for reporting, segmentation and insights
🍴Splice - Sharing Side by Side Information
Later your uncle asks them for some yoga studio recommendations. Your sister and your cousin go through the ones they've been to and finally find a few things to disagree on so they decide to combine their advice into a single text side my side for him to compare.
In Data Terms: A Splice places two datasets side by side instead of merging the data which could distort the structure. Aligning the data horizontally.
Why use a Splice?
✅ Useful for comparing or presenting different data
✅ Keeps data distinct but visible together
Building Connections:
Whether you’re managing guest lists or datasets, the principles are the same: combine what belongs together, connect what shares context, and enrich where needed.