Firebase stores data differently from a SQL database. Plan for that early and you avoid a painful rebuild later.
Your app runs slowly, your queries cost too much, or a simple change means moving a lot of data. In a NoSQL database like Firestore, those problems often trace back to how the data was laid out.
We design Firestore and Realtime Database schemas, the plans for how your data is stored. Each one is built around how your app reads and writes, what it needs to query, and how far it needs to scale.
A schema built around your query patterns
Planned data duplication that speeds up reads
Subcollections and collection groups arranged for your data
Composite indexes planned for complex queries
We list how your app reads and writes data, since that decides the shape of the schema.
We use subcollections for parent-child data, references for lookups, and duplicate data only where it’s read together.
We add composite indexes and pagination, so queries stay fast and read costs stay under control.
For an existing app, we write scripts that move your data into the improved schema.
We’ve built Firebase solutions for applications of various scales. We understand how to plan for scale, and in a NoSQL database that planning starts with the schema.
NoSQL design starts from your queries, where SQL design starts from tidy, separate tables. Data is often copied across documents to avoid costly joins. We design schemas based on how your app reads and writes data.
Firestore has no joins, so we use subcollections for parent-child relationships, document references for lookups, and planned duplication for data that’s often queried together.
Yes. We audit your current schema, find the bottlenecks and structural issues, design an improved schema, and build migration scripts to move your data.
We design the schema around your query patterns, create the right composite indexes, use collection groups for queries across subcollections, and add pagination to control read costs.
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