Five Reasons Why Postgres Should Be Your Data Warehouse in a Modern Data Stack

Many teams assume they need a cloud data warehouse such as Redshift, BigQuery, or Snowflake in order to build a modern data stack. This assumption leads to unnecessary complexity and cost, especially for companies with fewer than one hundred employees. The truth is that Postgres already contains everything most teams need for a high performance data warehouse. It is stable, predictable, cost effective, and perfectly suited for analytical workloads under five terabytes.

In this guide you will learn why Postgres should be the default data warehouse for lean data engineering teams and small to midsize businesses.

1. Your Workloads Are Predictable

If you are not dealing with hundreds of users hitting your database in unpredictable patterns, Postgres will work perfectly well. Most data engineering workloads run on a schedule such as nightly dbt jobs, hourly refreshes, or daily reports. This is where Postgres performs best because the load is steady and predictable. Even with a few analysts submitting queries throughout the day, a well configured Postgres replica will handle the activity without problems.

2. Your Total Data Barely Breaks Five Terabytes and Your Largest Table Is Under One Terabyte

Many teams assume they have entered the world of big data as soon as they reach fifty to one hundred gigabytes. This assumption is both inaccurate and expensive. A single terabyte of data is still manageable, and Postgres is fully capable of storing and querying tables of that size. Postgres supports relation sizes up to thirty two terabytes, and with proper indexing, keys, partitioning, and basic tuning, Postgres handles multi hundred gigabyte or even terabyte scale tables smoothly.

Your total data footprint is almost certainly within the comfort zone of Postgres.

3. Your Workload Does Not Require Sub Second Results

Real time analytics is often treated as a requirement even when it is not. Most dashboards do not need live streaming data. Stakeholders typically check reports once or twice per day, and dashboards usually refresh on predictable intervals such as every fifteen to sixty minutes. If your workflows can wait a few seconds or even a few minutes for results, Postgres will meet your needs without issue.

The speed requirement is usually a product expectation rather than a strict technical constraint.

4. Your Data Models Are Relational and Batched

If your data lands in structured tables such as fact and dimension models, and if it arrives in scheduled batches, Postgres is the ideal solution. Even when your sources originate from a stream, if that data is written into storage and then processed into table inserts on a schedule, you are still operating a batch oriented system.

If your data resembles relational tables rather than a continuous firehose, Postgres is the correct choice.

5. Your Data Engineering Team Is Small

Modern lakehouse architectures contain many moving parts such as storage buckets, serverless SQL engines, data catalogs, governance layers, table formats (more on this here) and distributed compute environments. Small teams should focus on delivering business value rather than managing unnecessary infrastructure. Postgres offers a simple operational model, a familiar SQL environment, and a single place to maintain the warehouse. This allows your team to work on insights rather than platform maintenance.

Conclusion

If you want a simple and reliable data warehouse for your modern data stack, Postgres should be your first choice. Your analytical workloads are predictable. Your data volume does not require a distributed engine. Your team benefits from a unified operational model. Your dashboards do not need real time performance. Postgres provides a fast and efficient foundation without the complexity of storage buckets, serverless SQL engines, or multi vendor architectures.

Before investing in a heavy cloud warehouse, start with Postgres. It delivers everything most teams need in a data warehouse while keeping your platform clean, manageable, and cost effective.