A complete comparison
of Starburst and EMR
What is Starburst Galaxy?
Starburst Galaxy is a price-performant, fully-managed, multi-cloud data lake analytics platform powered by Trino, a leading open-source distributed MPP SQL query engine. 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, query volume, and query complexity increases. Galaxy runs federated queries across the data lake, cloud data warehouses, on-premise databases, and relational data management systems like PostgreSQL and MySQL. Galaxy also supports a wide range of business-critical capabilities for big data processing and analytics, such as fault-tolerant execution, smart indexing and caching, building, managing, and sharing of Data Products, machine learning (PyStarburst and integration with Ibis), cross-cloud/cross-region analytics, and universal search and schema discovery.
What is EMR Trino?
As one of over 200 AWS services, Amazon EMR, formerly known as Elastic MapReduce is a managed cluster platform that simplifies running big data frameworks, such as Apache Hadoop, Apache Spark, PrestoSQL, and Trino on AWS to process and analyze vast amounts of data. Using these frameworks and related open-source projects, you can process data for analytics purposes and business intelligence workloads. Amazon EMR also lets you transform and move large amounts of data into and out of other AWS data stores and databases, such as Amazon S3 and Amazon DynamoDB. PrestoSQL was renamed to Trino in December 2020. Amazon EMR versions 6.4.0 and later use the name Trino, while earlier release versions use the name PrestoSQL. Their Serverless option pivots on running big data applications on the Amazon Web Services Cloud using open source frameworks while letting Amazon EMR Serverless configure, optimize, secure, and manage clusters for their customers.
Make your big data
analytics easier.
Not harder.
AWS EMR Comparison Factors to Consider
When looking for a data lakehouse solution, you should look for one that lets you pick your open formats, easily works with your data in and around the data lake, and is a hybrid solution, supporting on-premises and in the cloud data storage.
Simplicity
Going beyond key platform governance and management capabilities, a modern data analytics platform empowers data teams with easy-to-use functionality that increases productivity without adding complexity. It allows you to use a range of existing investments in just a few clicks. It allows you to build federated data products from distributed data sets to support business use cases and create and scale self-service usage and adoption across the organization.
Access
True data access empowers organizations with the ability to use all their data, no matter where it lives, across data lakes, data warehouses, and databases while having confidence in security and governance controls. True access is about meeting business needs on time while adhering to regulatory data sovereignty requirements. Your modern data lake analytics platform/lakehouse should free your data sources for analytics purposes, not confine them in another way.
Scalability
Internet scale matters in an internet-powered world but not every workload needs that power and performance. A modern data lake analytics platform puts the control in your hands to ensure high-performance scalability is available at a click of a button or automatically when you need it most while optimizing price-to-performance for all analytics workloads and maintaining confidence that queries will execute as scheduled.
Optionality
Open file and table formats are table stakes in providing optionality. A modern data lake analytics platform goes beyond the fundamentals to ensure your business has full control over your data by accessing data where it lives, allowing choice in cloud providers, security, and BI tools, and ensuring expert Trino support is available if and when your teams need it most.
Simplicity
Going beyond key platform governance and management capabilities, a modern data analytics platform empowers data teams with easy-to-use functionality that increases productivity without adding complexity. It allows you to use a range of existing investments in just a few clicks. It allows you to build federated data products from distributed data sets to support business use cases and create and scale self-service usage and adoption across the organization.
Query sharing
Supports AWS Glue and other Data Catalogs
Fully managed SaaS platform
Automated AWS compute plane set-up
Automated cluster management
Multi-cloud platform
Built-in data security
Built-in real-time usage, monitoring, and reports
Build-in data profiling
Built-in data lineage
Automated upgrades to the latest version of Trino
In platform one-click client connectivity
Data Products
Data Products sharing
GenAI text-to-SQL
Automated data lake optimization
Access
True data access empowers organizations with the ability to use all their data, no matter where it lives, across data lakes, data warehouses, and databases while having confidence in security and governance controls. True access is about meeting business needs on time while adhering to regulatory data sovereignty requirements. Your modern data lake analytics platform/lakehouse should free your data sources for analytics purposes, not confine them in another way.
Cloud and on-premises data federation
Built-in end-to-end encryption
RBAC/ABAC
AWS Service Account
AWS Lake Formation
Third-party access controls
Enhanced connectors for data access
Cross-cloud and cross-region analytics
In platform universal search and schema discovery
SSO via AWS IAM, Okta, Azure AD, and Google
Column masking and row-level filters
Time-based policies
Streaming ingest
Scalability
Internet scale matters in an internet-powered world but not every workload needs that power and performance. A modern data lake analytics platform puts the control in your hands to ensure high-performance scalability is available at a click of a button or automatically when you need it most while optimizing price-to-performance for all analytics workloads and maintaining confidence that queries will execute as scheduled.
Ad-hoc and interactive queries
Graceful and idle shutdown
Consistently execute long-running batch queries
Automated scaling for cost and performance optimization
Automated resizing a running cluster
Autoscaling by nodes
Automated cluster provisioning and sizing
Complex expression pushdown on top of OS Trino
Enhanced Fault Tolerant Execution (FTE)
Smart indexing and caching
Materialized Views
Parallel Connectors
Results and repeated subquery caching
Optionality
Open file and table formats are table stakes in providing optionality. A modern data lake analytics platform goes beyond the fundamentals to ensure your business has full control over your data by accessing data where it lives, allowing choice in cloud providers, security, and BI tools, and ensuring expert Trino support is available if and when your teams need it most.
Supports popular open table formats (Apache Iceberg, Delta Lake, Apache Hudi, and Apache Hive)
Supports popular open file formats
OS Trino as query engine
Open source Trino Python client
Run on multiple clouds
Expert in-house Trino support
Supports Python Dataframe API
Supports AWS Private Link, Azure Private Link, and Google Cloud Private Service Connect
More resources
Data Products for Dummies
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Gartner® Hype Cycle™ for Data Management 2023
Starburst has been recognized as a 2023 Gartner Hype Cycle Sample Vendor
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