Data Warehouse
A data warehouse is a centralized database designed for analytical queries and reporting. It consolidates data from multiple source systems into a single place where analysts and applications can run large queries, build dashboards, and generate reports.
The analytical system of record
Traditional data warehouses like Snowflake, BigQuery, and Redshift are built on columnar storage and optimized for read-heavy analytical workloads. They separate analytical processing from the transactional databases that run applications, so neither workload compromises the other.
The classic data warehouse follows a model of extract, transform, load (ETL) or extract, load, transform (ELT). Data flows from operational systems into the warehouse, where it is cleaned, modeled, and made available for querying. Modern warehouses increasingly work with open formats like Parquet and Iceberg on object storage.
The line between data warehouses and operational analytical databases has blurred. Systems like Arc handle high-volume ingestion and fast analytical queries in a single engine, which means some workloads that used to require a full warehouse can now run on a purpose-built columnar database.
How Arc handles Data Warehouse
Arc is not a traditional data warehouse, but it overlaps with warehouse workloads for time-stamped analytical data. It ingests at high throughput, stores data in open Parquet, and answers analytical SQL queries fast. For event data, telemetry, and observability, it can replace a warehouse layer at a fraction of the cost.
Related terms
Arc is a SQL-native time-series database. Open Parquet on storage you own, single Go binary, production-ready in 30 seconds.