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SaaSConstruct
AWS cloud template for building SaaS applications in one day
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AWS Boilerplates
Explore 1 boilerplate in this collection. Find the perfect starting point for your next project.
Other Database Integrations25
Why Choose AWS Boilerplates?
AWS provides a powerful data storage solution with specific transaction models, indexing strategies, and query capabilities suited for SaaS applications. Our AWS boilerplates implement database-native features—from ACID transactions to advanced indexing—with schemas optimized for AWS's query engine and scaling characteristics.
AWS boilerplates are designed around the database's data modeling approach and transaction semantics. They leverage AWS-specific features like JSONB columns, full-text search, aggregation pipelines, or partition keys depending on the database type. The schema design follows AWS's best practices for normalization (SQL) or document structure (NoSQL), with strategic indexes on query-heavy columns. Migration systems use AWS-native tools for version-controlled schema evolution.
Key Benefits
- AWS-native features (JSONB, aggregations)
- AWS-optimized schema design
- AWS indexing strategies
- AWS transaction patterns
- AWS-specific query optimization
- AWS scaling architecture
- AWS backup and replication
- AWS migration tooling
Browse our collection of 1 AWS boilerplate to find the perfect starting point for your next SaaS project. Each boilerplate has been carefully reviewed to ensure quality, security, and production-readiness.
Frequently Asked Questions
What AWS-specific features are leveraged in these boilerplates?
AWS boilerplates utilize the database's native capabilities including its transaction model (ACID for SQL, eventual consistency for NoSQL), indexing strategies (B-tree, GiST, full-text search), and advanced features like JSON columns, array types, window functions, or document queries. The schema design takes advantage of AWS's strengths—whether that's PostgreSQL's JSONB, MySQL's full-text search, MongoDB's aggregation pipeline, or Redis's data structures.
How is the AWS schema designed for SaaS applications?
AWS boilerplates include production-tested schemas for multi-tenancy, user management, subscriptions, and billing. The design follows AWS's best practices for data modeling—whether that's normalized tables with foreign keys (SQL), embedded documents vs. references (MongoDB), or partition key strategies (DynamoDB). Schemas include proper constraints, default values, and relationship management optimized for AWS's query engine.
What AWS query optimization techniques are implemented?
AWS boilerplates implement database-specific query optimizations including strategic indexing on frequently queried columns, query plan analysis, proper use of AWS's query features (prepared statements, query builders, aggregations), and N+1 query prevention. Connection pooling is configured for AWS's optimal settings, and caching layers are positioned to reduce database load while maintaining data consistency.
How does AWS scale in these boilerplates?
AWS boilerplates are structured for horizontal and vertical scaling using the database's native scaling features. This includes read replicas, sharding strategies (if applicable), connection pool sizing, and query optimization for distributed systems. The architecture supports AWS's scaling patterns—whether that's PostgreSQL's logical replication, MongoDB's sharding, or DynamoDB's automatic partitioning.
What AWS backup and migration strategies are included?
AWS boilerplates include migration systems using database-specific tools (Prisma migrations, Django migrations, Flyway, Liquibase, or native tools). They follow AWS's best practices for zero-downtime deployments, backward-compatible schema changes, and data migrations. Backup strategies leverage AWS's native backup features (pg_dump, mysqldump, mongodump) with automated scheduling and point-in-time recovery configurations.