Every bit in its place,
accessible in milliseconds.
Your database is the heartbeat of your application. We design schemas that scale, optimize queries that fly, and architect data pipelines that stay reliable under load.
Relational, document, and cache—
polyglot by design.
One size does not fit all. Relational databases excel at transactions and joins. Document stores offer flexibility and scale. Caches deliver speed. We combine them into a cohesive architecture. Explore our full web development practice.
Relational databases
PostgreSQL, MySQL, Oracle—ACID transactions, complex joins, and data integrity. Built for structured data and reporting.
Document stores
MongoDB, Firebase—flexible schemas, horizontal scaling, and nested data. Ideal for semi-structured content and real-time apps.
Key-value & cache
Redis, DynamoDB, Memcached—ultra-fast lookups and ephemeral storage. Perfect for sessions, caches, and leaderboards.
PostgreSQL, MySQL,
built for ACID.
Structured data, complex queries, and transactional integrity. PostgreSQL's advanced features (JSON, arrays, CTEs) and MySQL's wide deployment make them the backbone of most production systems.
Open-source, ACID, advanced features (JSON, arrays, CTEs)
Wide deployment, reliable, community support
Enterprise scale, complex queries, optimization tools
.NET ecosystem integration, T-SQL
MySQL fork, feature-rich, open-source
Managed relational—scale, backup, replica automation
MongoDB, Firebase,
flexible and fast.
Document stores excel at nested data, flexible schemas, and horizontal scaling. Realtime databases synchronize state across clients without manual polling. Learn more about our data pipeline and analytics practice.
Document store, flexible schema, aggregation pipeline
Realtime sync, auth built-in, NoOps—pay per read/write
AWS serverless, millisecond latency, pay per request
Google's document store—nested collections, real-time
JSON docs, replication, multi-master sync
Search and analytics—full-text, aggregations, dashboards
Redis, Neo4j, BigQuery—
domain-specific powerhouses.
In-memory caches for speed, graph databases for relationships, time-series databases for analytics. Each solves a specific problem better than a general-purpose database.
In-memory cache, pub/sub, atomic operations
Graph database—relationships, social networks, recommendations
Time-series OLAP—billions of rows, analytical queries
Distributed, geo-replicated, high write throughput
Google's petabyte warehouse—SQL on massive datasets
Cloud data warehouse—separate compute, storage, scaling
Data that scales,
stays secure, and endures.
Every database design anticipates growth, prioritizes consistency, and remains operable ten years from now.
Right tool for every pattern
Relational for ACID transactions, NoSQL for scale and flexibility, graph for relationships, cache for speed. Polyglot persistence without chaos.
Bulletproof data integrity
ACID guarantees where needed, constraint enforcement, foreign keys, and transactions. Your data stays consistent even during failures.
Query optimization at scale
Indexing strategy, query planning, query result caching, and read replicas. Serve millions of queries per second without breaking the bank.
Zero-downtime schema evolution
Add columns, rename fields, backfill data—all while the app keeps running. Tested migration scripts, rollback procedures built in.
Secure by default
Encryption at rest, TLS in transit, least-privilege roles, audit logging, and automated backups. Compliance (GDPR, HIPAA) baked in.
AI-driven optimization
Query analysis, missing index detection, slow query profiling, and cardinality estimation. Machines find what humans miss.
Answered by our
database engineers.
01Should we use SQL or NoSQL?
02How do you handle database scaling when data grows to millions of rows?
03What's your approach to database migrations and schema changes?
04How do you ensure backups and disaster recovery?
05Can you help optimize a slow database?
06How do we handle personally identifiable information securely?
Schemas that scale,
in weeks.
One discovery call to understand your data model, a normalized schema, and a working data pipeline with backups and replication. Ready for millions of rows.