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Join us for Datanova 2024, October 23-24th. We'll be discussing advancing analytics with Open Data Lakehouse innovations.
Learn moreStarburst 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.
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.
“When comparing Starburst and Dremio, we found that Starburst surpassed Dremio with 2.5X greater performance. Starburst also invests in more and higher quality out-of-the-box connectivity that unlocks Data Federation, which is key for us to achieve scalability at a lower TCO.”
– Anonymous, SVP of Big Data Capabilities
“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, 7bridges
Learn More
“When evaluating Starburst and Dremio, the underlying zero migration risk was a big differentiator with Starburst, and there’s a greater sense of confidence knowing the platform is built and operated by Trino experts. Other criteria, such as scalability, concurrency, and a seamless Tableau integration, also made Starburst the right choice for us.”
— Anonymous, Director of Engineering
“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.”
— Alberto Miorin, Engineering Lead
Learn More
“We chose Starburst over Dremio because Starburst is the only platform that meets our requirements within areas such as Credential pass-through, RBAC, and user impersonation on Teradata. More importantly, Starburst has proven to provide us with the best performance for federated queries and data lake analytics, so we can make faster decisions on all of our data.”
– Anonymous, Head of Data
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:
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.
Starburst Galaxy
Dremio Cloud
Data products
Data products
Built-in Natural Language Processing
Built-in Natural Language Processing
*
Automated data lake optimization
Automated data lake optimization
Built-in universal data sharing (internal and external)
Built-in universal data sharing (internal and external)
*
Automated AWS compute plane set-up
Automated AWS compute plane set-up
Managed Iceberg tables
Managed Iceberg tables
*
Enterprise grade 24x7 support
Enterprise grade 24x7 support
Comparison based on publicly available information as of July 8, 2024.
* In preview. Contact us to learn more.
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.
Starburst Galaxy
Dremio Cloud
Role-based access control (RBAC)
Role-based access control (RBAC)
Row-level filters and column masking
Row-level filters and column masking
Attributed based access control (ABAC), 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
Multi-region access control and governance
Time-based access control
Time-based access control
Integration with AWS Lakeformation
Integration with AWS Lakeformation
Multi-cloud data catalog and searchability
Multi-cloud data catalog and searchability
Popular data sources for federation
Popular data sources for federation
Multiple cloud regions across AWS, Azure, and GCP
Multiple cloud regions across AWS, Azure, and GCP
Optimized connectors - parallelism, cached views, dynamic filtering, and security and authentication
Optimized connectors - parallelism, cached views, dynamic filtering, and security and authentication
Streaming ingest
Streaming ingest
Data product governance
Data product governance
Comparison based on publicly available information as of July 8, 2024.
* In preview. Contact us to learn more.
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.
Starburst Galaxy
Dremio Cloud
Interactive query performance
Interactive query performance
Autoscaling
Autoscaling
Batch query support
Batch query support
High concurrency
High concurrency
Autoscaling by adding/removing incremental nodes
Autoscaling by adding/removing incremental nodes
Enhanced Fault Tolerant Execution (FTE)
Enhanced Fault Tolerant Execution (FTE)
Cache resilience
Cache resilience
*
Smart indexing and caching for files and text data
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.
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.
Starburst Galaxy
Dremio Cloud
Open source MPP SQL query engine
Open source MPP SQL query engine
Supports popular file formats
Supports popular file formats
Supports all major open table formats
Supports all major open table formats
Data federation with first- and third-party data catalogs
Data federation with first- and third-party data catalogs
Dataframe API for Python
Dataframe API for Python
*
Support for Apache Ranger
Support for Apache Ranger
*
Cross-cloud/cross-region analytics
Cross-cloud/cross-region analytics
In platform migration of Hive to Iceberg/Delta Tables
In platform migration of Hive to Iceberg/Delta Tables
Natively run SQL on Iceberg, Delta Lake, Hudi, and Hive table formats
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.
Access and analyze your data with elastic scale and high performance your business demands. Take Starburst Galaxy for a free test drive, watch the on-demand demo (no form fill needed), or contact us.
Dremio is used to run interactive and ad-hoc analytics on federated data. However, with Starburst, you can access more data sources, cross-cloud and cross-region analytics, internet scale performance, universal search and discovery, enterprise-grade support SLAs, and more.
Dremio and Starburst offer an open data lakehouse platform that provides self-service SQL analytics, data warehouse performance and functionality, and data lake flexibility across your data. However, with Starburst, you gain a similar experience across AWS, Azure, and GCP, more data sources for federated data products, and a highly performant MPP SQL query engine with optimized open-source Trino.
Similar to Starburst, Dremio supports a wide range of data types for analysis. Some of the data types that can be analyzed include:
However, Starburst can support additional types via plugins. Connectors to data sources are not required to support all Trino data types described. If there are data types similar to Trino’s that are used on the data source, the connector may map the Trino and remote data types to each other as needed.
It is Dremio’s SaaS-based GUI data lakehouse tool. Compute is deployed in the customer’s own cloud account. Dremio Cloud is supported on AWS in 5+ cloud regions (Microsoft Azure is in preview). Compute can only be provisioned in the region selected at setup; customers may incur costs even when not writing queries.
© Starburst Data, Inc. Starburst and Starburst Data are registered trademarks of Starburst Data, Inc. All rights reserved. Presto®, the Presto logo, Delta Lake, and the Delta Lake logo are trademarks of LF Projects, LLC
Up to $500 in usage credits included