Low latency queries in data lakehouses refers to the practice of minimizing end-to-end query response time through a combination of table design, file organization, caching strategy, and engine optimization. Achieving consistently low latency is critical for interactive BI dashboards, real-time operational applications, and the tight reasoning loops of agentic AI systems.
Table Design for Low Latency
The most impactful factor for query latency is often the physical layout of data in Iceberg, established at write time:
- Partition Design: Partitioning Iceberg tables by the most common filter columns (date, region, customer segment) enables partition pruning, which skips entire directories of files at metadata scan time, before any data is read.
- Sort Order and Z-Ordering: Sorting table data by frequently filtered secondary columns clusters related data within Parquet row groups, enabling row-group pruning for fine-grained filters within a partition.
- File Sizing: Optimal Parquet file sizes (typically 128MB to 512MB) balance metadata overhead (too many small files) against parallelism efficiency (too few large files). Files in this range are processed efficiently by both single-machine engines like DuckDB and distributed engines like Dremio.
Engine Choices for Low Latency
For the lowest query latency on well-structured Iceberg tables, purpose-built analytical engines consistently outperform general-purpose engines. Dremio's combination of C3 columnar caching, Data Reflections, vectorized Arrow execution, and a mature Cost-Based Optimizer regularly achieves sub-second latency for BI dashboards over terabyte-scale Iceberg tables that would take minutes to scan with a naive approach.
