You want to run interactive, high-performance BI dashboards and analytics directly on your data lake storage. Which SQL query technology should you use? Here’s what you should consider in comparing Dremio to Starburst.
Dremio powers mission critical BI dashboards which demand low latency analytics, without the need for ETL pipelines and data copies. Starburst supports ad-hoc workloads ONLY.
Dremio provides self-service with a shared semantic layer for all users and tools. Starburst does not provide a semantic layer or data curation capabilities.
Dremio provides high performance with cost effectiveness. Dremio is on average 2x-3x faster than Starburst with significant cost savings on compute.
| Feature | Dremio | Starburst |
| Workload Focus | Ad-hoc, mission critical BI, and everything in between directly against the data lake | Ad-hoc |
| Architecture | Dremio | Starburst |
| Memory format | Columnar, Apache Arrow | Columnar, custom format |
| Physical workload isolation model | Single cluster, multiple engines | Multiple clusters |
| Scalability | Auto scale feature to scale up/down to handle any fluctuations in workload | Key functionalities around auto-scaling are limited to enterprise edition |
| Transparent Acceleration | Robust and proven (Data Reflections) | Very limited |
| Pushdowns with Relational DBs | Yes | Yes |
| High performance NVMe acceleration | Yes (Columnar Cloud Cache) | Yes, but limited |
| Data Curation & Semantic Layer | Yes | No |
| Data Lineage | Yes | No |
| User Interface | Yes, user-friendly UI supporting all features | Yes, but very limited capabilities |
| SQL Engine | Arrow in-memory with Gandiva LLVM compilation to machine code for perf | Java-based compilation only |
| LLVM Compilation to Machine Code | Yes (Gandiva) | No (Java only) |
| Predictive Reads for S3/ADLS Data Sets | Yes | No |
In benchmarking reports, Dremio provides significant performance and cost advantages over Starburst.
Dremio is a SQL lakehouse platform that enables high-performance business intelligence (BI) and analytics directly on data lake storage. Dremio simplifies data engineering and eliminates the need to copy data into proprietary data warehouses and create cubes, aggregation tables and BI extracts, providing flexibility and control for data architects and data engineers, and self-service for data consumers.
Starburst is the commercial version of the open source Trino (formerly PrestoSQL). Trino is a SQL-based query engine that gives users the ability to run analytics on data lakes. Starburst is fundamentally designed to handle non-interactive use cases such as ad-hoc workloads.
Without a query acceleration layer, Starburst requires data engineers to copy and move subsets of data into cloud data warehouse (CDW) or data mart platforms and create cubes or aggregation tables. Starburst may work for federated queries over disparate data sources but it’s not designed for interactive, BI workloads that demand high concurrency and low latency.
Leap selected Dremio to provide self-service access to collected data from thousands of energy meters, dramatically improving analyst productivity, reducing data engineering workload, and improving the quality and timeliness of business decisions.
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Download this white paper to get a step-by-step roadmap of Dremio adoption. At each step, you’ll learn about benefits gained, as well as the complexities and risks reduced, as workloads are migrated from traditional systems to Dremio.
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Find out more about Dremio Cloud, the only data lakehouse platform built for SQL and built on open source technologies that both data engineers and data analysts love. Dremio powers BI dashboards and interactive analytics directly on data lake storage.
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