A complete comparison
of Starburst and Dremio
What is Starburst?
Starburst offers a full-featured open data lakehouse platform, built on open source Trino – the MPP SQL query engine used by some of the largest internet companies. Built by the creators of OS Trino (formerly PrestoSQL), the Starburst platform enables teams to discover, govern, organize, analyze, and share data with self-service analytics in on-premises, hybrid, or cloud-centric data architectures. Starburst is used for both interactive ad-hoc analytics, long-running workloads like batch and ETL/ELT, streaming use cases, and building data products to power AI and GenAI applications.
What is Dremio?
Dremio is a data lakehouse platform providing self-service SQL analytics, data warehouse analytics and data lake flexibility. As the original creators of Apache Arrow, Dremio supports ad-hoc and interactive analytics.
We chose Galaxy because of the flexibility it offers to connect to so many different types of tools and data sources. Galaxy allows us to use Lakehouse tables for both transformations and reporting, and on top of that, Galaxy provides access to multiple data formats. This ensures that we can stay flexible and iterate quickly as the Lakehouse technology evolves.
Simon Thelin
Lead Data Engineer
The decision to deploy Starburst Enterprise was made simpler because it has proven to be a reliable, fast, and stable query engine for S3 data lakes.
Starburst is a Leader in Enterprise Big Data Analytics
Don’t take our word for it. Starburst is named #1 for Quality of Support and Ease of Use in G2 Crowd’s Grid Report based on real customer reviews. Additionally, customers said Starburst beat out Dremio in all of these categories:
- Meets Requirements
- Ease of Use
- Ease of Admin
- Quality of Support
- Data Visualization
- Multi-Source Analysis
Simplicity
Going beyond platform governance and management capabilities, an open data lakehouse empowers data teams to increase productivity without adding complexity, maximize existing data architecture investments in just a few clicks, and allows teams to easily build, manage, and share data products from over 20+ data sources – creating a single version of the truth.
Data products
Built-in Natural Language Processing
Automated data lake optimization
Built-in universal data sharing (internal and external)
Automated AWS compute plane set-up
Managed Iceberg tables
Enterprise grade 24x7 support
Comparison based on publicly available information as of July 8, 2024.
* In preview. Contact us to learn more.
Access
Empower data teams with the ability to securely use all their data assets, no matter where they live, across data lakes, data warehouses, and databases – on-premises or across clouds. With your open data lakehouse, easily discover, create, govern, share, and collaborate on curated data sets by connecting your data silos before, during, and after your modernization journey.
Role-based access control (RBAC)
Row-level filters and column masking
Attributed based access control (ABAC), role-based access control (RBAC), row-level filters, and column masking
Multi-region access control and governance
Time-based access control
Integration with AWS Lakeformation
Multi-cloud data catalog and searchability
Popular data sources for federation
Multiple cloud regions across AWS, Azure, and GCP
Optimized connectors - parallelism, cached views, dynamic filtering, and security and authentication
Streaming ingest
Data product governance
Comparison based on publicly available information as of July 8, 2024.
* In preview. Contact us to learn more.
Scalability
An open data lakehouse should offer high concurrency and puts the control in your hands to ensure performant scalability is available when you need it most, while optimizing price-to-performance for all analytics workloads.
Interactive query performance
Autoscaling
Batch query support
High concurrency
Autoscaling by adding/removing incremental nodes
Enhanced Fault Tolerant Execution (FTE)
Cache resilience
Smart indexing and caching for files and text data
Comparison based on publicly available information as of July 8, 2024.
* In preview. Contact us to learn more.
Optionality
An open data lakehouse goes beyond the basics of open file and table formats by providing choice in hybrid or cloud environments, more data federation, seamless cross-cloud and cross-region analytics, choice in data catalogs without compromising the user experience, and provides an enhanced MPP SQL query engine based on open standards and is supported by the largest internet companies in the world.
Open source MPP SQL query engine
Supports popular file formats
Supports all major open table formats
Data federation with first- and third-party data catalogs
Dataframe API for Python
Support for Apache Ranger
Cross-cloud/cross-region analytics
In platform migration of Hive to Iceberg/Delta Tables
Natively run SQL on Iceberg, Delta Lake, Hudi, and Hive table formats
Comparison based on publicly available information as of July 8, 2024.
* In preview. Contact us to learn more.
More resources
Start for Free with Starburst Galaxy
Up to $500 in usage credits included
Discover
Easily search across data sources and clouds to find the data you need.
Govern
Streamline data governance with built-in RBAC and ABAC.
Analyze
Run internet-scale workloads with the power of Trino.
Fast
Accelerate queries with smart indexing and caching technologies like Warp Speed.
More Deployment Options
