A database isn't something you fill in afterwards. It determines how quickly your application keeps running as you grow, how secure the data is, and how expensive hosting becomes over five years. We design, build and maintain databases that grow with your product, from schema design for a new SaaS to performance tuning of a slow legacy system.
Many development teams treat the database as an implementation detail of the application. That works fine, until the first hundred thousand users arrive, the first audit comes knocking, or the first migration to the cloud is on the table. Then it turns out the schema isn't indexed, read replicas aren't configured, and there's no audit log.
We treat database development as a discipline in its own right. Whether we're designing a schema for a greenfield product, tuning a slow production database, or migrating a legacy Oracle environment to PostgreSQL, we work from the demands of the domain, not from whichever ORM the developers happen to be used to. The result is a data layer that scales predictably, can be maintained by your team, and doesn't need redesigning within a year.
We work both within our broader cloud-native platform development and as standalone database engagements. Many of our clients come to us with a specific problem (a query that's too slow, a migration that feels risky, an audit requirement that has suddenly appeared) and don't want a completely new platform straight away.
What we do depends on where your organisation stands. A new application needs different attention from a ten-year-old system that has suddenly stopped scaling.
For new SaaS products, e-commerce platforms or custom applications, we help design the data model. Together we decide which entities, relationships and indexes are needed, which columns may be polymorphic, and which normalisation choices will keep the system manageable five years from now. We build in multi-tenant design (row-level security or schema-per-tenant), audit logging and an encryption strategy from the very start.
The classic call: "our app has become slow over the past year." We analyse slow query logs, EXPLAIN plans and index statistics. We tackle N+1 problems, rewrite poorly written joins, add missing indexes and, where needed, review the connection pool configuration. Often we achieve order-of-magnitude improvements without the schema having to change fundamentally.
Oracle to PostgreSQL, on-premise SQL Server to Aurora, splitting a monolithic database, or moving from SQL to a hybrid model with document or time-series storage. We map the data model, design the target architecture, and carry out the migration in phases using dual-write or CDC so that production keeps running. We often work alongside our platform migration teams.
No loose dump files or a Confluence page. A working production database, documented in a way your own team can carry on with.
Not every database question is a service in its own right. But we keep recognising these patterns, and this is where dedicated database work makes the difference between patching symptoms and solving the underlying problem.
The application was fast, but with every release another quarter of a second of response time creeps in. The slow-query log keeps growing, users complain, and the developers are afraid to touch the schema.
You are reaching the limits of what a single instance can handle. Sharding, partitioning, read replicas or a split between OLTP and OLAP workloads becomes unavoidable.
Oracle licences are expensive, SQL Server has to move to the cloud, or an outdated MySQL 5.7 needs replacing. You don't want a big-bang migration, but a phased move with no downtime.
GDPR deletion, the right to be forgotten, retention policies, audit logging of who changed what. Solving this too crudely in the application layer is a mistake; it belongs at database level.
Your multi-tenant platform is growing, and the current approach (a single tenant_id column with queries that filter on it) is becoming fragile. Row-level security or schema-per-tenant becomes necessary.
You want to add an AI feature using retrieval-augmented generation, semantic search or similarity matching. That calls for pgvector, a dedicated vector database, or a hybrid approach where both run side by side.
We map out the current model: schema, data volumes, slow queries, monitoring data and usage patterns. For a greenfield project, we do the same but based on the use cases and read/write patterns you anticipate.
Which database suits this domain? PostgreSQL is our default, but sometimes ClickHouse for analytical queries, TimescaleDB for time-series data, or MongoDB for heavily nested documents. We justify each choice and set the alternatives side by side.
The data model is presented as an ERD, a migration script and a testable schema definition. Your team can review it, suggest indexes, and see how queries will perform under load through benchmarks on realistic volumes.
For new databases: build, connect to the application, and load testing. For migrations: dual-write or CDC so production keeps running, followed by a controlled cut-over. We always do a dry run before going to production.
Dashboards, alerts and runbooks for incident response. Your team receives training on the chosen tools, and we either hand over management entirely or keep it under an ongoing contract with us.
We are not tied to a single vendor or stack. We advise based on what best suits your domain, team and existing systems.
PostgreSQL is our standard first choice for custom applications: a rich feature set, strong standards, no licensing lock-in and excellent cloud support. We also work with MySQL and MariaDB (particularly in existing LAMP stacks), Aurora within AWS, Oracle in legacy contexts, and SQL Server in Microsoft environments.
For heavily nested documents we use MongoDB or CouchDB. Redis, Valkey and DynamoDB for caching and session storage. TimescaleDB or InfluxDB for sensor and metric data. Neo4j or ArangoDB for relationship-heavy problems such as fraud detection or recommendations. For search functionality we choose Elasticsearch, OpenSearch or Meilisearch, depending on scale and budget.
For analytical workloads and data warehouses we use ClickHouse, Apache Druid, BigQuery, Snowflake or Databricks. See also our work on real-time analytics platforms. For AI/RAG implementations, pgvector within PostgreSQL or a dedicated vector store such as Pinecone or Weaviate. For extremely high write volumes, Cassandra or ScyllaDB.
Database work is a craft with its own toolkit. Below are the tools that almost always come up in our projects. The list is not exhaustive, but it is representative.
What clients usually want to know before we start on database work.
A no-obligation introductory call of half an hour. Tell us where your database is getting stuck, which product you want to build, or which migration is on the table. We'll listen, ask questions and give you direction you can use, even if it turns out we're not the right partner.
Appfront uses cookies and similar technologies to keep the website working properly, for analytics and for marketing. You choose what you allow. Read more in our privacy policy.
