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A complete comparison of Starburst and Dremio

Discover how Starburst and Dremio compare across platform access, scalability, simplicity, and optionality, including real customer reviews and G2 Crowd ratings.

2024 GigaOm Radar ReportCompare now

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.

Fortune 100 Financial Services Organization

 

“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

7bridges

 

“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

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Gaming Technology Company

 

“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

Zalando

 

“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

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Leading Multinational Bank

 

“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

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 
G2 Comparison Report

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.

Starburst Galaxy

Dremio Cloud

Data products

Data products

GenAI text-to-SQL

GenAI text-to-SQL

*

Built-in universal data sharing

Built-in universal data sharing

*

Automated data lake optimization

Automated data lake optimization

Enables automated data maintenance across data compaction, profiling and statistics, vacuuming, and data retention*

Automated AWS compute plane set-up

Automated AWS compute plane set-up

Pre-deployed and managed by Starburst
Manual, installed and managed by customer

Cross-cloud and cross-region analytics

Cross-cloud and cross-region analytics

Requires separate Dremio instances to be deployed and self-managed in each region separately

Comparison based on publicly available information as of March 18, 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.

Starburst Galaxy

Dremio Cloud

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

Access control and governance limited to a single region

Time bases access control

Time bases 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

Starburst Galaxy supports over 20 popular data source connectors including Oracle, S3, ADLS, BigQuery, Salesforce, and more - see here for a complete list -> https://docs.starburst.io/starburst-galaxy/catalogs/index,
Missing sources like BigQuery, Azure Synapse, MongoDB, Salesforce, and others

Multiple cloud regions across AWS, Azure, and GCP

Multiple cloud regions across AWS, Azure, and GCP

supports 25+ regions across AWS, Azure, and GCP
Supports only 5 AWS regions

Optimized connectors - parallelism, cached views, dynamic filtering, and security and authentication

Optimized connectors - parallelism, cached views, dynamic filtering, and security and authentication

Some connectors benefit from advanced relational pushdown

Streaming ingest

Streaming ingest

Galaxy continuously runs the processing, ingestion, and analysis of stream data from Kafka, ensuring your data is ready for analysis within minutes of initial collection.*

Comparison based on publicly available information as of March 18, 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.

Starburst Galaxy

Dremio Cloud

Interactive query performance

Interactive query performance

Batch query support

Batch query support

Enhanced Fault Tolerant Execution (FTE) provides right sized clusters for all queries
With a single cluster

Autoscaling

Autoscaling

AWS Edition only

High concurrency

High concurrency

Scalable performance for hundreds of concurrent queries
Scalability issues supporting concurrency with larger cluster sizes

Autoscaling by adding/removing incremental nodes

Autoscaling by adding/removing incremental nodes

Auto-scaling of an engine is accomplished by adding additional engine replicas, these are clusters, which represent the unit of scale. This means that a Large sized engine would auto-scale by adding another identical Large engine a time, up to the maximum number of replicas allowed for the engine. This can lead to unnecessary costs for unnecessary compute resources

Comparison based on publicly available information as of March 18, 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.

Starburst Galaxy

Dremio Cloud

Open source MPP SQL query engine

Open source MPP SQL query engine

Uses OS Trino formerly known as PrestoSQL
Dremio Sonar is a proprietary SQL engine based on Apache Arrow

Supports popular file formats

Supports popular file formats

AVRO, CSV, JSON, ORC, Parquet
ORC and AVRO are not supported

Supports all major open table formats

Supports all major open table formats

Supports Apache Iceberg, Apache Hudi, Delta Lake, and Apache Hive
Only supports Apache Iceberg and Delta Lake

Supports popular data catalogs

Supports popular data catalogs

Supports AWS Glue, Hive Metastore, Unity Catalog, and Starburst native catalog, Gravity, and JDBC and Rest-based Iceberg catalog*

Only works with Dremio Arctic catalog (proprietary) or AWS Glue. Does not support Hive Metastore, Unity Catalog, or JDBC catalogs for Iceberg implementations

Dataframe API for Python

Dataframe API for Python

*

Enterprise grade 24x7 support

Enterprise grade 24x7 support

Support for Apache Ranger

Support for Apache Ranger

In public roadmap, contact us to learn more.

Comparison based on publicly available information as of March 18, 2024.

* In preview. Contact us to learn more.

Free test drive | Watch | Contact us

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More resources

Analyst Report

GigaOm

2024 GigaOm Radar for Data Lakes and Lakehouses

Market Report

Gartner

Market Guide for Analytics Query Accelerators

Analyst Report

Concurrency Labs

Concurrency Labs: Querying 15 Billion Records

Customer Success Story

Doxo

Enhanced agility, materialized views, and efficient database analytics

Some additional exploration

What is Dremio used for?

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.

What kind of tool is Dremio?

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.

Is Dremio an ETL tool?

Similar to Starburst, Dremio supports a wide range of data types for analysis. Some of the data types that can be analyzed include:

  • Numeric data types such as DECIMAL, INT, BIGINT, FLOAT, and DOUBLE
  • String and binary data types, such as VARCHAR and VARBINARY
  • Boolean data type BOOLEAN
  • Date and time data types such as DATE
  • Semi-structured data types like LIST and STRUCT, as well as data type mappings for external sources, time zone support, and coercions support

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.

 

What is Dremio Cloud?

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.

Start Free with
Starburst Galaxy

Up to $500 in usage credits included

  • Query your data lake fast with Starburst's best-in-class MPP SQL query engine
  • Get up and running in less than 5 minutes
  • Easily deploy clusters in AWS, Azure and Google Cloud
For more deployment options:
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