Postgres for products that matter.
Danubio designs, optimizes, scales, and operates Postgres systems in production. Schema design for new products, query optimization on slow systems, replication and partitioning at growth, and the operational discipline that keeps these databases honest at scale.
One team, across new builds, modernization, and the years after.
What Postgres is good at.
Postgres is the default database for serious product work, and the default is right more often than the trend cycle suggests. These are the project shapes where Danubio reaches for it.
Relational and document in one database.
JSONB columns plus first-class indexes make hybrid models tractable. You do not pick between structured and document; Postgres handles both well, and the application layer stops working around the choice.
Battle-tested at every scale.
From single-instance prototypes to multi-TB clusters, Postgres has the operational maturity newer databases have not yet earned. The patterns, the tooling, and the talent pool are all there.
First-class extensions.
PostGIS for geo, pg_vector for embeddings, TimescaleDB for time-series, pg_partman for partitioning. The base database extends naturally for adjacent problem shapes without a second system to operate.
Strong consistency by default.
ACID transactions, MVCC, real foreign keys, real check constraints. Data integrity does not require eventual-consistency workarounds at the application layer or out-of-band reconciliation jobs.
The workloads Danubio runs on Postgres.
The data shapes Postgres is most at home with, drawn from the production systems we design, scale, and keep running.
Transactional product data layers.
SaaS app data, marketplace transactions, billing systems, audit trails. Workloads where consistency, query flexibility, and developer ergonomics matter more than peak read throughput.
Analytical reporting on operational data.
Materialized views, partitioned fact tables, derived aggregates, BI integrations. One database serves both the transactional and the reporting needs without a second pipeline.
Search and similarity with extensions.
Full-text search, geographic queries, vector retrieval for AI products. Postgres extensions remove the need for a separate search infrastructure when the volume does not yet require one.
Multi-tenant data architectures.
Schema-per-tenant, row-level security, tenant-aware connection pooling. The database carries the tenancy story, so the application code stays focused on the product.
What Danubio has shipped in Postgres.
From a single-tenant consultancy back-office to an enterprise SaaS scaled to 350,000 users. Postgres carries the data layer across very different products, industries, and access patterns.
A custom back-office for a one-person training consultancy
Adam Weisbart ran a global Scrum training practice as a sole operator. Danubio built him a custom console where calendars, students, finances, travel, and feedback all lived in one place. He used it daily from 2018 to 2023.
Rebuilding a live multi-tenant platform without downtime
CORE Home was live with pilot brokerages when Danubio took ownership of the stack. The rebuild migrated tenants one at a time onto a stronger foundation, with no cutover and no broken clients. The same platform now carries 5,000+ tenants across web and mobile.
Scaling an acquired SaaS to 350K enterprise users
Inside Real Estate acquired dashCMA in 2020 and kept Danubio on as the engineering team. Five years and 1M+ presentations later, the product is used across major national brokerages, including RE/MAX, eXp, and Berkshire Hathaway HomeServices.
Internationalizing a crypto marketplace to 40+ languages
Paxful was a peer-to-peer crypto marketplace operating globally. Over two years, Danubio built its localization layer - externalization, locale-aware formatting, RTL, and a Crowdin-backed release pipeline - that took the platform to 40+ languages.
From a founder’s vision to acquisition in 18 months
How a founder-led PropTech product moved from first build to acquisition, with Danubio acting as the engineering team behind the product.
What we use across Postgres, end to end.
Versions, extensions, replication, and operational tooling we actually run in production. Versions track current; older installations get rationalized as part of the engagement.
Versions and tooling
- PostgreSQL 15, 16, 17, 18
- psql, pgAdmin
- pg_dump and pg_basebackup
- Connection pooling (PgBouncer, PgCat)
- pg_stat_statements baked in
Extensions
- PostGIS for geographic data
- pg_vector for embeddings
- TimescaleDB for time-series
- pg_partman for partitioning
- pgcrypto and citext
Replication and HA
- Streaming replication
- Logical replication
- Hot standby and failover
- pg_repack and pg_squeeze
- Point-in-time recovery
Migration and operations
- Flyway and Liquibase
- Online schema migrations
- pgBadger and slow-query analysis
- EXPLAIN ANALYZE workflows
- Backup verification and DR drills
The way Danubio approaches Postgres work.
Principles that shape every Postgres engagement, drawn from years of running production data layers where corruption, downtime, and silent drift are real risks.
- 01
Senior-led, every engagement.
The engineers designing Postgres schemas and tuning queries for a Danubio client are the engineers who have run Postgres in production for years. No training-on-the-job at the client's expense, and no tuning recipes copied from a blog post.
- 02
Schema as a first-class artifact.
Every schema change ships as a versioned migration, reviewed in pull requests and rolled out through a pipeline. No casual ALTER TABLE in production, and no implicit changes from ORM auto-generation that drift the spec.
- 03
Indexes earn their place.
Every new index is analyzed against real query plans before merge. Unused indexes cost write throughput; we audit existing indexes and prune the ones that no plan touches.
- 04
Migration safety first.
No destructive operations on running systems without explicit rollback plans. Online schema changes for tables that cannot afford locks. Backups verified, not just taken.
- 05
Observability before tuning.
pg_stat_statements, slow query logs, EXPLAIN ANALYZE plans. Performance work follows what the database is actually telling us, not the developer's hunch about what feels slow.
A Postgres workload on the table?
New schema design, slow-query investigation, replication setup, version migration, partitioning a large table, or a multi-tenant architecture rebuild. Whatever stage the database is at, we can talk through it.