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Apache Doris: Real-Time Analytics at Scale for AI Agents
A petabyte-scale OLAP database built for sub-second queries. Cuts analytics latency and infrastructure cost—useful if you're shipping agent backends or data-heavy products.
apache/doris ↗Apache Doris is a database optimized for answering complex questions across massive datasets in under a second. Think of it as a search engine for structured data—you throw millions of rows at it, and it returns aggregates, trends, or specific records instantly.
Why builders care: If you're shipping an AI agent or analytics product, you need fast answers on live data. Doris handles petabyte-scale workloads without requiring separate pipelines for real-time and batch processing. The trade-off is it's not a transactional database (no frequent row updates)—it's optimized for read-heavy, analytical queries. Compare it to ClickHouse or Druid if you're evaluating; Doris has tighter AI-agent integration and lower operational overhead.
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Apache Doris: Real-time analytics DB built for AI agents—sub-second queries at petabyte scale
If your product needs to answer questions on live data fast, Doris is worth a test. It's an OLAP database (analytical queries, not transactional) that handles petabyte workloads with sub-second latency. One pipeline, no separate batch layer. Real draw: AI-agent optimization and lower infra cost than Druid or ClickHouse for certain use cases. Open source, Apache 2.0.
Apache Doris hits different if you're building agent backends. Petabyte-scale analytics in <1s, no separate batch pipeline, built-in AI optimization. OLAP, not OLTP—if your product is "answer questions on live data," worth a sprint. github.com/apache/doris
For founders shipping data-driven products: Apache Doris is a real-time OLAP database optimized for AI agents. Queries on petabyte-scale data return in <1s. Single pipeline (no batch layer), lower operational overhead than traditional analytics stacks. Open source, Apache 2.0. Worth evaluating if analytics latency is friction.
Been running analytics on petabyte-scale datasets, and the latency tax is real. Just pulled Apache Doris into a project—sub-second queries on data that would choke a traditional warehouse. It's a hybrid OLAP engine built for real-time. Designed for AI agents and analytics backends, not just dashboards. → Scales to petabytes without the infra bloat → Sub-second response times on complex aggregations → Native support for semi-structured data (JSON, arrays) → Drop-in replacement for ClickHouse or Druid in most stacks Why it matters: if you're shipping agent backends or data products, you need query latency under a second. Most setups require separate hot/cold tiers or eventually hit the wall. Doris handles both real-time ingestion and analytical queries in one layer. Getting started is straightforward—Docker or Kubernetes, standard SQL interface, standard connectors. No exotic ops. The payoff: faster analytics, simpler infra, lower cost per query. GitHub: https://github.com/apache/doris
just shipped a petabyte-scale analytics layer using Apache Doris. sub-second queries on data that would break traditional warehouses. it's a hybrid OLAP engine—real-time ingestion + analytical reads in one system. built for agents and data backends. → scales to petabytes → sub-second aggregations → native JSON/array support → one tier instead of hot/cold splits if you're running agent backends or data products, this cuts latency and infra cost. standard SQL, standard connectors, kubernetes-ready. no exotic ops. just faster analytics. https://github.com/apache/doris