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 Report

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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.

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

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

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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

Built-in Natural Language Processing

Automated data lake optimization

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

Built-in universal data sharing (internal and external)

Automated AWS compute plane set-up

Pre-deployed and managed by Starburst

Manual, installed and managed by customer

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.

Starburst Galaxy

Dremio Cloud

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

Access control and governance limited to a single region

Time-based access control

Integration with AWS Lakeformation

Multi-cloud data catalog and searchability

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

supports 25+ regions across AWS, Azure, and GCP

Supports only 5 AWS regions and missing GCP

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

Some connectors benefit from advanced relational pushdown

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.

Data product governance

supports 25+ regions across AWS, Azure, and GCP

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.

Starburst Galaxy

Dremio Cloud

Interactive query performance

Autoscaling

Batch query support

Enhanced Fault Tolerant Execution (FTE) provides right sized clusters for all queries

Limited with a single cluster

High concurrency

Scalable performance for hundreds of concurrent queries

Scalability issues supporting concurrency with larger cluster sizes

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

Enhanced Fault Tolerant Execution (FTE)

Offers a more scalable solution with its FTE feature, capable of handling up to 60TB of data, making it suitable for very large and memory-intensive workloads

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.

Starburst Galaxy

Dremio Cloud

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 all major open table formats

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

Only supports Apache Iceberg and Delta Lake

Data federation with first- and third-party 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

Support for Apache Ranger

Cross-cloud/cross-region analytics

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

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.

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More resources

GigaOm

2024 GigaOm Radar for Data Lakes and Lakehouses

Starburst and Iceberg

Icehouse architecture, combination of Iceberg and Trino, for the best data lakehouse

Data Products Executive Guide

Understanding the value data products can bring to your data and AI strategies

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.