Your database is the foundation of your product. We help you choose the right database technology, design schemas that perform at scale, and implement data strategies that grow with your business.
Every feature your product offers ultimately reads from or writes to a database. The design of your database, including schema structure, indexing strategy, query patterns, and technology selection, determines how fast those features perform, how reliably they operate, and how easily new features can be added. Database design consulting ensures this foundation supports rather than constrains your product.
Database design mistakes are among the most expensive to fix because they affect every part of the application. A poorly normalized schema forces data duplication and consistency headaches. Missing indexes cause queries to scan entire tables. Inappropriate technology choices create mismatches between your data model and your query patterns.
At Arthiq, we have designed databases for social media management platforms, invoicing systems, and AI agent tools. Each domain has distinct data requirements: high-write-throughput for social media events, strict consistency for financial records, and efficient similarity search for AI retrieval. This range of experience informs our consulting with pattern recognition across diverse use cases.
The database landscape offers dozens of options across relational, document, key-value, graph, time-series, and vector categories. We help you cut through the noise and select the technology that matches your data model and access patterns.
PostgreSQL is our default recommendation for most applications because of its flexibility, performance, and ecosystem maturity. It handles relational data, JSON documents, full-text search, and even vector operations, reducing the need for additional specialized databases. We add MongoDB for truly schema-less workloads, Redis for caching and real-time features, ClickHouse for analytical queries, and Pinecone or pgvector for AI similarity search.
We evaluate managed versus self-hosted options based on your team operational capacity, cost sensitivity, and customization needs. Managed services like Amazon RDS, Google Cloud SQL, and PlanetScale reduce operational burden at the cost of some flexibility and higher per-unit pricing. Self-hosted options provide maximum control but require dedicated database administration skills.
Schema design balances normalization for data integrity against denormalization for query performance. We design schemas that are normalized for transactional workloads where data consistency is paramount, and strategically denormalized for read-heavy workloads where query speed is the priority.
We model your entities, relationships, and access patterns before writing any schema definition language. This modeling phase identifies the queries your application will run most frequently and designs the schema to serve them efficiently. We also plan for anticipated growth, choosing data types and structures that accommodate future requirements without schema rewrites.
Migration strategy is part of our schema design. We design schemas with forward-compatible migration in mind, using techniques such as nullable columns, default values, and additive changes that allow schema evolution without downtime. For applications that require zero-downtime migrations, we design dual-write patterns and background migration processes.
Indexes are the primary tool for database performance tuning, but they are not free. Each index speeds up reads at the cost of slower writes and additional storage. We design indexing strategies that optimize for your actual query patterns, adding indexes that provide significant read improvement while minimizing write overhead.
We analyze your most frequently executed and most resource-intensive queries using EXPLAIN plans and query profiling. For each problematic query, we evaluate whether the solution is a better index, a rewritten query, a schema adjustment, or a caching layer. Often, a combination of approaches produces the best result.
We also implement query monitoring that alerts on new slow queries as they appear. As your application evolves, new features introduce new query patterns that may not be well-served by existing indexes. Continuous monitoring catches these issues before they affect user experience.
Databases are often the first system component to reach scaling limits. We design scaling strategies that extend your database capacity as your product grows, from simple optimizations to distributed architectures.
The first scaling lever is query optimization and caching, which often provides an order of magnitude improvement without architectural changes. Read replicas handle the next growth phase by distributing read queries across multiple database instances. Connection pooling with tools like PgBouncer prevents connection exhaustion under high concurrency.
For applications that outgrow a single primary database, we design partitioning strategies that distribute data across shards. Horizontal sharding requires careful key design and introduces complexity in cross-shard queries, so we implement it only when simpler approaches are insufficient. We also evaluate whether different workloads, such as transactional and analytical, should be served by separate database systems using a command query responsibility segregation pattern.
Your database design determines how fast your product can be and how easily it can grow. We help you get it right from the start or fix what is holding you back.