Querying data directly from cloud object storage (like Amazon S3) was historically too slow for interactive Business Intelligence (BI) dashboards. Dremio overcame this latency by engineering a completely reimagined query execution architecture designed from the ground up to minimize I/O overhead and maximize CPU utilization.
Apache Arrow & Vectorized Execution
At the core of Dremio's performance is Apache Arrow, an open-source, in-memory columnar data format co-created by Dremio's founders. Instead of processing data row-by-row (like legacy systems), Dremio's execution engine uses Vectorized Execution to process entire batches (vectors) of columnar data simultaneously in CPU cache. By keeping data in the Arrow format throughout the entire execution lifecycle, Dremio eliminates the CPU-intensive serialization and deserialization steps that cripple other engines.
C3: Columnar Cloud Cache
Even with advanced execution, retrieving data over the network from S3 involves inherent latency. Dremio mitigates this with C3 (Columnar Cloud Cache). As data is read from remote object storage, Dremio automatically caches the most frequently accessed Parquet files on the high-speed local NVMe SSDs of the execution nodes. If a subsequent query requests the same data, Dremio reads it directly from the local cache at NVMe speeds, effectively turning cloud storage into a high-performance local disk.
Predictive Pipelining
Because cloud object storage suffers from high "time-to-first-byte" latency, Dremio utilizes Predictive Pipelining. The engine intelligently anticipates which data blocks will be needed next during a query scan and issues asynchronous pre-fetch requests to the storage layer. By the time the CPU finishes processing the current batch of data, the next batch has already arrived in memory, ensuring the CPU is never starved waiting for network I/O.
Iceberg Integration
Dremio's performance is deeply intertwined with Apache Iceberg. Dremio utilizes Iceberg's metadata manifests for aggressive Data Skipping. Before a query ever touches physical data, Dremio's query planner uses Iceberg's min/max statistics to prune away irrelevant files, ensuring that only the absolute minimum amount of data is ever loaded into the execution engine.
