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Starburst vs. Databricks
Discover how Starburst and Databricks compare across platform access, scalability, simplicity, and optionality, including real customer reviews and G2 Crowd ratings.
A real open data lakehouse lets you pick your open format. Works with your data in and around the data lake, on-premises and in the cloud. Is easy to use.
What is Starburst Galaxy?
Starburst Galaxy brings the open data lakehouse architecture to life using open file and table formats and an optimized open MPP SQL query engine with OS Trino. Starburst Galaxy is used for both interactive ad-hoc analytics and long-running workloads like batch and ETL/ELT, and offers high scalability and query completion rates even as the amount of data (terabytes of data), query volume, and query complexity increases. It runs federated queries across data lakes, cloud data warehouses, on-premises databases, and relational data management systems like PostgreSQL and MySQL. Galaxy also supports enhanced fault-tolerant execution, smart indexing and caching, Data Products, universal search and schema discovery while truly separating compute and storage between Starburst and your cloud storage.
What is Databricks SQL?
Databricks, traditionally known for its Apache Spark origins and data science and machine learning capabilities, introduced Databricks SQL, available as a serverless and non-serverless data warehouse in the Databricks Data Intelligence Platform (formerly known as the Lakehouse Platform) to run SQL and BI applications using its proprietary Photon engine. It offers a governance model via a proprietary data catalog, use of open formats (Delta Lake being the preferred for a first-class experience), APIs, and integrations with third party tools. It provides general compute resources for SQL queries, visualizations, and dashboards that are executed against the tables in data in cloud object stores. It also enables find and share insights with the built-in SQL editor, visualizations and dashboards.
What do our customers have to say?
Genus
“While other solutions, such as Databricks, were considered, none were as seamless and performant as Starburst, the fully supported, production-tested and enterprise-grade distribution of open source Trino.”
— Patrice Linel, Senior Manager of Data Science & Data Engineering
Learn more
Banco Inter
“Databricks would work with our data lake platform, but if we chose Databricks, we would have to cancel all the initiatives that we had in our big data platform using AWS. So we are betting on the AWS stack with Starburst and we think we’re going to be successful in this journey.”
— Carlos Pedrosa, Superintendent of Information Technology Governance
Learn MoreStarburst is a Leader in Big Data Processing and Distribution
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 Databricks in all of these categories:
- Quality of Support
- Ease of Admin
- Ease of Use
- Meets Requirements
- Breadth of Partner Applications
- Product Direction
- Data Visualization
- Performance and Reliability
Simplicity
A simple and intuitive experience includes everything from simple pricing models, ease of governance and management, minimal overhead and time to get started, a simplified customization and configuration experience, and most importantly just easy to use by any SQL user — a PhD should be optional.
Starburst Galaxy
Databricks SQL
Built-in data security
Built-in data security
Automated cluster management
Automated cluster management
Built-in real-time usage monitoring
Built-in real-time usage monitoring
Automated data maintenance
Automated data maintenance
*
GenAI text-to-SQL
GenAI text-to-SQL
*
Get started within 10 minutes from first sign-on
Get started within 10 minutes from first sign-on
Simple pricing
Simple pricing
Comparison based on publicly available information as of November 30, 2023
*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. With your modern data lake analytics platform 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
Databricks SQL
Universal search
Universal search
Schema discovery
Schema discovery
Role and attribute-based access controls
Role and attribute-based access controls
Data lineage
Data lineage
Column/row masking
Column/row masking
Automatic end-to-end encryption
Automatic end-to-end encryption
Private link for AWS, Azure, and Google
Private link for AWS, Azure, and Google
*
SSO for client connectivity
SSO for client connectivity
*
Automatic data classification
Automatic data classification
*
Streaming ingest
Streaming ingest
Integration with AWS LakeFormation
Integration with AWS LakeFormation
Supports popular data sources for federation
Supports popular data sources for federation
Supports hybrid data architectures
Supports hybrid data architectures
Optimized connectors for federation
Optimized connectors for federation
First-class federation for multiple data catalogs
First-class federation for multiple data catalogs
Federated data products
Federated data products
Data product sharing
Data product sharing
Time-based access control policies
Time-based access control policies
Data observability
Data observability
Data profiling
Data profiling
Metadata tagging
Metadata tagging
Comparison based on publicly available information as of November 30, 2023
*In preview. Contact us to learn more.
Scalability
A modern data lake analytics platform with high concurrency 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
Databricks SQL
High concurrency
High concurrency
Fault tolerant execution (FTE)
Fault tolerant execution (FTE)
Ad-hoc and interactive queries
Ad-hoc and interactive queries
Materialized views
Materialized views
Autoscaling by adding/removing incremental nodes
Autoscaling by adding/removing incremental nodes
Customizable scaling for cost and performance optimization
Customizable scaling for cost and performance optimization
Smart caching
Smart caching
Results and repeated subquery caching
Results and repeated subquery caching
*
Smart indexing
Smart indexing
Index and cache resilience
Index and cache resilience
Comparison based on publicly available information as of November 30, 2023
*In preview. Contact us to learn more.
Optionality
A modern data lake analytics platform takes the lakehouse architecture 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 offering an enhanced MPP SQL query engine based on open standards and supported by the largest internet companies in the world.
Starburst Galaxy
Databricks SQL
Run on multiple clouds
Run on multiple clouds
Supports popular open file formats
Supports popular open file formats
Standard ASNI SQL
Standard ASNI SQL
Supports Python
Supports Python
Dataframe API for Python
Dataframe API for Python
*
OS MPP SQL query engine
OS MPP SQL query engine
First-class data federation with first and third-party data catalogs
First-class data federation with first and third-party data catalogs
Natively run SQL on Iceberg, Delta Lake, Hudi, and Hive table formats
Natively run SQL on Iceberg, Delta Lake, Hudi, and Hive table formats
Built-in cross-cloud and cross-region querying
Built-in cross-cloud and cross-region querying
Supports hybrid cloud architecture
Supports hybrid cloud architecture
*
Supports Apache Ranger
Supports Apache Ranger
In platform capability to migrate Hive and Delta tables to Iceberg
In platform capability to migrate Hive and Delta tables to Iceberg
Comparison based on publicly available information as of November 30, 2023
*In preview. Contact us to learn more.
Contact us | Tutorial | Try
Access and analyze your data with elastic scale and high performance your business demands. Get started with a free Galaxy trial, watch this tutorial on Starburst Galaxy, or contact us.
More resources
Some additional exploration
What is Databricks SQL used for?
Unlike traditional SQL warehouses, Databricks SQL is a platform that provides SQL data warehousing capabilities on data stored in a cloud data lake. It is primarily used for data exploration, ad hoc analytics (without the need of data pipelines) and interactive big data analytics. The platform allows users to query data from a limited number of federated data sources using non-optimized connectors and requiring the use of their proprietary data catalog, Unity Catalog, for an optimal data federation experience.
Databricks SQL also offers its own visualization tooling and integration with various visualization BI tools such as Tableau, PowerBI, ThoughtSpot, Looker, and others to make it easier for data analyst to consume the data.
What is a Databricks workspace?
A workspace within the Databricks platform is an environment for data engineers, data science teams and others to access Databricks resources with the option of running a single or multiple workspaces within your Databricks account.
What type of SQL is used in Databricks?
Similar to Starburst, Databricks SQL uses the American National Standards Institute (ANSI) for SQL, which is a common standard for most SQL databases.
Can I write SQL in Databricks?
Yes. Similar to Starburst Galaxy, within Databricks SQL there is an environment via SQL endpoints for running SQL queries, creating dashboards with visualization tools, and sharing query results.
Can I write Databricks SQL in a text editor?
Yes, similar to Starburst Galaxy, you can write your SQL queries or scripts in any text editor of your choice and then copy and paste them into Databricks SQL or a Databricks notebook for execution.
Is Databricks SQL a program?
No, Databricks SQL is not a program. It is a SQL tool within the Databricks Lakehouse platform.
How does Azure Databricks differ from Databricks on AWS or GCP?
Databricks on Azure or Azure Databricks is the result of a collaboration between Microsoft and Databricks to bring Databricks on as a first party service to Microsoft Azure. The core functionality of Databricks remains the same across the three hyperscale cloud providers but there are some differences in how it is integrated with each cloud provider’s services.
- Azure Databricks
- Azure treats Databricks as a first party service and is built and supported in collaboration with the Databricks team.
- Azure Databricks can be integrated into the Azure portal and can be provisioned with a single click.
- Azure Databricks is highly optimized for the Azure environment as a result of the shared engineering, is tightly integrated with other Azure services.
- Time will tell how Azure Databricks evolves with Azure Synapse and Azure Fabric.
- Databricks on AWS and GCP
- Databricks on AWS or GCP is not a first party service like Azure Databricks. Therefore, similar to other SaaS tools, it requires a bit more setup and understanding of AWS and GCP services like IAM (for AWS) and networking.
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