20 Database-Focused Projects (2026)

Databases are the backbone of every application. Knowing how to design schemas, write efficient queries, index correctly, and migrate data safely is what separates a junior developer from a senior one. These 20 projects focus entirely on the data layer — from relational schema design for a library catalog to geospatial queries and audit logging systems. Most projects work with PostgreSQL, with some options for MongoDB and Redis.

1. Library Catalog (Relational Schema) — Difficulty: ⭐⭐ Skills: entity-relationship modeling, normalization, foreign keys Design and implement a relational database for a library catalog. Features: tables for books, authors, publishers, members, loans; many-to-many book-author relationship; prevent double-borrowing with constraint; overdue loan query.

2. E-Commerce Database Design — Difficulty: ⭐⭐ Skills: normalized schema, product variants, pricing history Design a database for an e-commerce store. Features: products with multiple variants (size, color), category tree with parent-child relationships, order and line item tables, pricing history table for sale tracking, inventory per variant.

3. Social Media Data Model — Difficulty: ⭐⭐⭐ Skills: complex relationships, follower pattern, feed generation Design a database for a social media platform. Features: users with profile data, follow relationship (directed graph), posts with likes and comments, share/bookmark features, denormalized follower count for performance.

4. Hotel Booking Schema — Difficulty: ⭐⭐⭐ Skills: date range queries, exclusion constraints, availability Design a database for a hotel booking system. Features: rooms with types and amenities, guest reservations with check-in/check-out dates, exclusion constraint to prevent overlapping bookings, dynamic pricing by season.

5. Inventory Management System — Difficulty: ⭐⭐ Skills: stock tracking, warehouse locations, transactions Design a database for tracking inventory across warehouses. Features: products with SKU and barcode, warehouse and location tables, stock movement transactions (in/out/transfer), reorder level triggers, inventory count reconciliation.

6. Event Ticketing DB — Difficulty: ⭐⭐⭐ Skills: seat reservations, isolation levels, deadlock prevention Design a database for an event ticketing system. Features: venues with seat maps, events with multiple shows, seat reservation with expiration (15-min hold), prevent double-booking with SELECT FOR UPDATE, waitlist queue.

7. Multi-Tenant SaaS Database — Difficulty: ⭐⭐⭐⭐ Skills: row-level security, tenant isolation, shared vs separate DB Design a multi-tenant database for a SaaS application. Features: shared database with tenant ID column, PostgreSQL Row-Level Security policies, tenant-specific custom fields via JSONB, per-tenant query limits.

8. Time-Series Sensor Data — Difficulty: ⭐⭐⭐ Skills: time-series tables, partitioning, downsampling, retention Design a database for storing IoT sensor readings. Features: time-series table with sensor ID and timestamp, automatic partitioning by month, downsampling queries (hourly/daily averages), data retention policy with automatic cleanup.

9. Graph Database for Recommendations — Difficulty: ⭐⭐⭐ Skills: recursive CTEs, graph traversal, relationship scoring Build a recommendation system using PostgreSQL recursive CTEs or a dedicated graph DB. Features: user-product interaction graph, “users who bought X also bought Y” query, depth-limited traversal, weighted relationship scoring.

10. Full-Text Search Implementation — Difficulty: ⭐⭐⭐ Skills: GIN indexes, tsvector, tsquery, ranking Implement full-text search using PostgreSQL’s built-in text search. Features: tsvector generation with weight assignment, tsquery with @@ operator, ranking with ts_rank, search suggestions from pg_trgm, language-specific stemming.

11. Database Migration Tool — Difficulty: ⭐⭐⭐⭐ Skills: schema versioning, up/down migrations, rollback Build a simple database migration tool (like a mini Flyway or Alembic). Features: versioned SQL migration files, track applied migrations in a schema table, execute up/down commands, detect and warn on destructive changes.

12. Query Performance Analyzer — Difficulty: ⭐⭐⭐⭐ Skills: EXPLAIN ANALYZE, query planning, index suggestions Build a tool that analyzes SQL query performance. Features: run EXPLAIN ANALYZE and parse the output, identify sequential scans vs index scans, suggest missing indexes, show query cost breakdown, highlight slow operations.

13. Redis Caching Layer — Difficulty: ⭐⭐⭐ Skills: cache-aside pattern, TTL, invalidation, serialization Build a Redis caching layer in front of a PostgreSQL database. Features: cache-aside (lazy loading) pattern, configurable TTL per query, cache invalidation on write, serialization with JSON or MessagePack, hit/miss ratio monitoring.

14. MongoDB Aggregation Pipeline — Difficulty: ⭐⭐ Skills: $match, $group, $lookup, $unwind, $bucket Build a set of MongoDB aggregation pipelines for an e-commerce dataset. Features: sales totals grouped by month, customer segmentation by order value, product category cross-selling analysis, running totals with $sum, pagination with $skip/$limit.

15. PostgreSQL Partitioning Demo — Difficulty: ⭐⭐⭐ Skills: declarative partitioning, partition pruning, maintenance Implement PostgreSQL table partitioning for a large dataset. Features: range partitioning by date, list partitioning by category, partition pruning with EXPLAIN, automatic partition creation with pg_partman, detach old partitions for archiving.

16. Database Backup/Restore Tool — Difficulty: ⭐⭐⭐ Skills: pg_dump, pg_restore, compression, automation Build a CLI tool that automates database backup and restore. Features: scheduled pg_dump to compressed file, incremental and full backup modes, restore to a specified point in time, S3/GCS upload integration, backup verification.

17. CRUD Generator — Difficulty: ⭐⭐⭐ Skills: schema introspection, code generation, templates Build a tool that reads your database schema and generates CRUD API code. Features: introspect tables and columns, generate REST endpoints and SQL queries, support for joins and foreign key lookups, configurable output format (Express, FastAPI).

18. Data Warehouse Star Schema — Difficulty: ⭐⭐⭐⭐ Skills: dimensional modeling, fact tables, dimension tables, ETL Design a data warehouse using a star schema. Features: sales fact table with date, product, store, customer dimensions; slowly changing dimension (SCD) type 2 for product changes; ETL script to transform from normalized tables; OLAP queries with GROUP BY ROLLUP.

19. Geospatial Queries (Nearby Places) — Difficulty: ⭐⭐⭐ Skills: PostGIS, spatial indexes, distance queries, geo-JSON Build a geospatial query system using PostGIS. Features: store locations with geometry type, find places within a radius (ST_DWithin), distance calculation (ST_Distance), sort by nearest, GeoJSON output for map integration.

20. Audit Logging System — Difficulty: ⭐⭐⭐ Skills: triggers, history tables, temporal queries, who changed what Build an audit logging system that tracks all data changes. Features: trigger-based logging on INSERT/UPDATE/DELETE, capture old and new values, store who made the change and timestamp, query change history for a specific row, revert to previous version.

How to Choose

If you’re new to database design, start with the library catalog (#1) and e-commerce schema (#2) to learn entity-relationship modeling. Move to the hotel booking (#4) and event ticketing (#6) for date-range constraints and concurrency. Projects 10 (full-text search), 15 (partitioning), and 19 (geospatial) cover PostgreSQL-specific features. Projects 13 (Redis) and 14 (MongoDB) expand to other data stores. The migration tool (#11), CRUD generator (#17), and audit logging (#20) are great for intermediate developers.

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