Unify batch and streaming data, accelerate BI analytics, and deliver trusted business insights at scale on a high-performance, real-time analytical database.
When the warehouse is unified, fast, real-time, and elastic, four things shift at once: the trust of the data, the speed of analysis, the freshness of decisions, and the cost of running it all.
Data from orders, customers, payments, CRM, ERP, ads, and logs often tells different versions of the same story. A modern data warehouse brings these signals together into one trusted analytical layer, so every team can work from consistent metrics and make decisions with confidence.
Three teams run Apache Doris as the analytical core of their data warehouse: at scale, with real concurrency, on live business data.
“Apache Doris cut our P95 query latency by nearly 70% and let us migrate 100% of ad-hoc and BI workloads off Presto, with far better stability and lower cost.”
“Apache Doris and Apache Paimon let us consolidate fragmented engines and storage into one lakehouse, with 6× faster queries and 5× higher concurrency than Presto.”
“Data updates can be completed within seconds, and queries can be responded to within hundreds of milliseconds.”
Five things a modern data warehouse has to be good at, and the specific Apache Doris capabilities that meet each one.
Modern data warehouses need to support detail records, fact and dimension tables, wide tables, aggregated metrics, and business-ready datasets. Strong modeling keeps definitions consistent, improves query performance, and makes trusted data reusable across teams.
Business teams need live visibility into what is happening now. That requires streaming ingestion, CDC, incremental updates, and fresh data that becomes queryable within seconds, not after yesterday’s batch.
Modern warehouse workloads need both incremental refresh and large-scale batch execution. Materialized views and incremental computation keep aggregates, rollups, and reporting tables fresh without full recomputation, while reliable batch processing supports end-of-day jobs, backfills, and historical workloads.
Operational databases, event streams, SaaS applications, and open lakehouse tables all contain critical business data. Modern warehouses need to integrate these sources and query data in place across formats like Iceberg, Hudi, Delta Lake, and Hive, without copying everything into another silo.
As the warehouse becomes the shared analytics foundation, it must be secure, reliable, auditable, and easy to operate. That requires fine-grained access control, workload isolation, high availability, audit logs, and simplified operations, so every team can safely depend on the same platform.
Support detail records, aggregated metrics, real-time upserts, and analytical datasets in one engine. Doris combines flexible table models with partitioning and bucketing to improve data organization, query performance, and reuse.
