OLAP (Online Analytical Processing)
OLAP, or Online Analytical Processing, describes database systems built to answer analytical questions over large volumes of data quickly. Think aggregations, group-bys, trends, and reporting across millions or billions of rows.
What OLAP is built for
OLAP systems are optimized for reading and summarizing large datasets. A typical OLAP query scans a lot of data and returns a small answer, like "total revenue by region last quarter" or "p99 latency by service over the past month." Speed on those scans is the whole point.
This is the opposite of OLTP, Online Transaction Processing, which handles many small reads and writes of individual records, like updating a single order or a user's balance. The two workloads pull database design in opposite directions, which is why they usually run on different systems.
Most OLAP databases are columnar, because storing data by column is what makes large aggregations fast.
How Arc handles OLAP (Online Analytical Processing)
Arc is an OLAP database for time-stamped data. It is built to ingest huge volumes and answer analytical queries fast, using a columnar engine over open Parquet. It is not an OLTP system, so it does not replace your transactional database. It complements it.
Related terms
Arc is a high-performance columnar database. Open Parquet on storage you own, single Go binary, production-ready in 30 seconds.