Combining data from multiple tables for no-code data joins

Combining data from multiple tables for no-code data joins

Building modern apps means pulling together data from different tables or collections to serve meaningful, connected info. We call this data joining. Whether you’re stitching together customer details with order info or syncing products to orders, joining multiple datasets is non-negotiable for rich, interactive experiences for no-code data joins.

Why joining data is essential

Databases often split info into separate tables to avoid duplication and keep things consistent. Think:

• customers hold the who
• orders track the what and when
• order_items get down to product specifics per order

No order summary feels complete without customers or items linked up. Joining data is how you unlock deep insights and satisfy complex queries.

Here’s the kicker: apps that skip this step are missing up to 75% of context behind their data.

Approaches to data joining

There are two major playbooks:

1. Backend Joins: Data gets combined on the server/database before it’s sent to your app.
2. Frontend Joins: The app pulls separate datasets and merges them client-side.

Each method suits different situations—think data volume, responsiveness, and team setup.

Backend joins: leveraging data source capabilities

Backend joining aggregates related data before it arrives on your device. This usually means:

• Less data sent over the wire
• Better speed and scalability under heavy loads
• Sensitive info filtered out before reaching the user

Common backend tools that play well with joins:

• Relational Databases (Postgres, MySQL): SQL joins are baked in with foreign keys linking tables.
• Supabase: Lets you fetch related tables in one go—like grabbing companies and their city names in a single query.
• Xano: Provides API “add-ons” to select tables, keys, and join names before publishing endpoints.

Others worth noting:

• Firestore: Uses subcollections or denormalized data for relationships.
• Airtable: Employs linked records for data connections.
• GraphQL: Supercharges nested queries to pull exactly the related data you want.

Backend join checklist

• Does your DB or API natively support joins?
• Can you request nested or joined data via your endpoint?
• Are your datasets large enough to justify server-side combining?
• Do your security policies require filtering on the server side?

Sexy stat: Backend joins typically yield 30-50% less data transfer, slashing app load times and client CPU use.

Scoreboard truth: For stable, voluminous data, backend joins are the workhorse.

Frontend joins: client-side data merging for api-driven relationships no-code

No backend join support? No problem. Frontend joins stitch data after retrieval.

Two key tactics:

1. Lookup by Key

Find the first item that matches a key. Example: find a user linked to an order.

lookup(order.user_id, usersCollection, "id")

• Search value: e.g., user ID
• Collection: all users
• Key field: user IDs

Returns the matched user or undefined.

2. Filter by Matching Key

Grab multiple items keyed to a single value. For example, fetch all appointments for one contact.

filterByKey(appointmentsCollection, "contact_id", contact.id)

• Collection: appointments
• Key to match: contact_id
• Value: contact's id

Returns an array of matching items.

Why frontend joins?

• Speedy, no backend changes needed.
• Perfect for prototyping or shifting criteria on the fly.
• Handles data from multiple unrelated sources.

Keep in mind: frontend merges can drag when data sizes get big — client performance can tank quickly.

Comparing backend and frontend joining methods

• Performance: Backend Joins - Faster, pre-filtered data only; Frontend Joins - Can slow down with larger datasets
• Data Transfer: Backend Joins - Minimal, only what’s needed; Frontend Joins - Full datasets transferred upfront
• Security: Backend Joins - Sensitive data filtered server-side; Frontend Joins - Client gets all data, raises risk
• Scalability: Backend Joins - Efficient with large volumes; Frontend Joins - Best for light, smaller datasets
• Flexibility: Backend Joins - Less dynamic; backend changes needed; Frontend Joins - Highly flexible; instant tweaks allowed
• Complex Logic: Backend Joins - Limited to SQL or backend language; Frontend Joins - Fully custom join logic allowed
• Dependency: Backend Joins - Requires backend knowledge and uptime; Frontend Joins - Independent, front-end controlled

When to use each joining strategy

Choose backend joins if:

• Your app handles large or growing datasets
• You want faster loads and less bandwidth
• Security demands sensitive data filtering before delivery
• Multiple app parts consume the same joined data often

Go with frontend joins when:

• Datasets are light and manageable on the client
• Join rules shift often or require dynamic tweaks
• You’re rapidly prototyping without backend friction
• Pulling data from multiple unrelated backends
• Backend support for joins is missing or limited

Mini-delta: Switching from frontend to backend joins can reduce load times by up to 60% with growing data sizes.

Conclusion

Joining data across tables or collections isn’t just ‘nice to have.’ It’s the backbone of relevant, interactive apps and an essential approach for no-code data joins.

Backend joins rule for performance and security on big, stable datasets. Frontend joins flex quick iteration and cross-source mashups.

Know your data size, security needs, and timeline. Play to the method that powers your product’s velocity and quality.

Remember: Lean joins make lean products.

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