Data Pipelines for Startups: Keep It Stupidly Simple

The modern data stack is absurdly complex: Fivetran for ingestion, Snowflake for warehousing, dbt for transformation, Airflow for orchestration, Looker for BI. Five tools, each with its own learning curve and pricing. For a startup with ten employees and a hundred thousand rows, this is architectural cosplay.

For startups under one million rows, your application database is your data warehouse. PostgreSQL is an astonishingly capable analytical database. Materialized views handle most reporting. Window functions handle most analysis.

Our standard pipeline: the app writes to PostgreSQL normally. Materialized views pre-compute analytical queries. A cron job refreshes views hourly or nightly. Metabase connects to a read replica and queries the views. Additional infrastructure: one read replica (twenty to forty dollars per month), one Metabase instance (free self-hosted or seventy-five dollars on cloud). Setup: one to two days.

For external data (Stripe, HubSpot, Google Analytics), we write simple ingestion scripts. Each runs on a cron, calls the API, upserts to a staging table, logs success or failure. Fifty to a hundred lines of TypeScript each, using idempotent upsert patterns so failures resume cleanly.

When do you outgrow this? Three inflection points. First, data exceeding what PostgreSQL handles for analytics (five to ten million rows in your largest table). Consider ClickHouse. Second, transformation logic complex enough that raw SQL maintenance becomes error-prone. That is when dbt adds value. Third, more than ten data sources with interdependencies. That is when Dagster adds value.

Cost comparison: "modern data stack" for a startup runs roughly one thousand to seventeen hundred per month. Our approach: thirty to one hundred five per month. A ten to fifteen times reduction.

Resist building sophisticated data infrastructure before you have sophisticated data problems. Start with PostgreSQL and materialized views. The data team you hire later will thank you for a simple, understandable system instead of half-configured Airflow with broken DAGs.