Real-Time Analytics
Real-time analytics is the practice of analyzing data as soon as it is generated, so that insights are available within seconds rather than after a batch job hours later. It powers live dashboards, alerting, and immediate decision-making.
What real-time really requires
The defining requirement is freshness. Data has to be queryable almost the instant it arrives, not after an overnight load. That puts pressure on two things at once: ingestion has to keep up with the incoming stream, and queries have to return fast over data that is still arriving.
This is hard for traditional warehouses, which are built around periodic batch loads. It is the natural territory of systems designed for streaming ingestion and fast analytical queries over fresh time-series data.
Use cases are everywhere: live ops dashboards, fraud and anomaly detection, trading, and any situation where a slow answer is a useless answer.
How Arc handles Real-Time Analytics
Arc makes data queryable about 100 milliseconds after it lands, so analytics run on fresh data, not yesterday's batch. Combined with fast columnar queries, that supports live dashboards and alerting on data as it streams in.
Related terms
Arc is a high-performance columnar database. Open Parquet on storage you own, single Go binary, production-ready in 30 seconds.