Starburst (Trino)
A Trino-based enterprise lakehouse analytics platform — analyze data where it lives, without moving it. Build your enterprise analytics environment with Starburst Enterprise, which adds security, governance, performance acceleration, operational automation, and 24×7 support on top of the open-source Trino engine, and Galaxy, the fully managed SaaS.
Starburst and Trino at a Glance
Starburst was founded by the creators of Trino (formerly PrestoSQL) and is the largest Trino expert organization, contributing roughly 84% of Trino code commits in 2024. It delivers a commercial distribution that adds enterprise security, governance, performance acceleration, and operations on top of open-source Trino, with 50+ connectors spanning open table formats such as Iceberg, Delta Lake, and Hive as well as RDBMS, NoSQL, and SaaS — enabling single-SQL analytics without data movement.
- Trino-based MPP query engine: always-on coordinator + worker architecture delivering interactive response on petabyte-scale data
- Data federation: 50+ connectors — Iceberg, Delta Lake, RDBMS, NoSQL, Kafka, SaaS — in a single SQL
- Warp Speed acceleration: smart indexing and caching for up to 5× faster queries and up to 10× less CPU
- Enterprise governance: row/column-level RBAC and ABAC, dynamic masking, Ranger integration, query audit
TPC-DS query performance (vs Hive)
Data source connectors
Trino commits contributed in 2024
Support · 30-min SLA · dedicated TAM
How Trino and Starburst Enterprise Relate
SEP is not a rewrite of Trino — it is a certified distribution that stacks enterprise layers on top of Trino. The engine is identical.
Trino
An open-source distributed MPP SQL engine (formerly PrestoSQL). An always-on coordinator + worker architecture provides interactive response on petabyte-scale data, releasing a new version roughly every two weeks.
Starburst
Founded by the creators of Trino, Starburst is the largest Trino expert organization, contributing about 84% of Trino code commits in 2024.
Same engine
SEP N-e ships the same engine as upstream Trino N — identical SQL semantics, cost-based optimizer (CBO), and connector SPI.
Expanded surface
On top sit security (BIAC, Ranger), exclusive connectors, Warp Speed acceleration, management UI and Insights, supported deployment paths, and 24×7 support contracts.
SEP = Trino engine + enterprise layers
Mental model: Trino is the engine, Starburst is the platform around it — raw engine performance is the same, and the difference comes from the layers surrounding the engine.
One Query Engine, Many Data Sources
Starburst reaches into each source directly and runs distributed queries without moving your data. JOIN lakehouse, databases, and SaaS in a single SQL statement.
Why Starburst — Four Core Values
Data Federation
JOIN multiple sources in a single SQL without moving or copying data — the need to pre-consolidate via ETL disappears altogether.
Iceberg Native
Full support for hidden partitioning, partition evolution, time travel, and ACID, with automated table maintenance such as compaction and snapshot expiration.
Performance & Reliability
Warp Speed smart indexing and caching delivers up to 5× faster queries and 10× less CPU; fault-tolerant execution (FTE) is hardened for batches up to 60TB.
Fine-Grained Security
Row/column-level RBAC and ABAC, dynamic column masking, Apache Ranger integration, and query audit — meeting regulated-industry requirements at the platform level.
Connectors — Reaching the Long Tail of Enterprise Data
Typical enterprise data lives in places like Oracle, Teradata, SAP, and Salesforce. That is why SEP-exclusive connectors account for much of the license value.
SEP-Exclusive Connectors (not in upstream Trino)
Snowflake · Teradata · IBM Db2 · SAP HANA · Salesforce · Azure Synapse · Greenplum · Netezza · Cosmos DB · DynamoDB · Splunk · Neo4j · Generic JDBC · Stargate (cluster-to-cluster federation)
Enhanced Versions of Upstream Connectors
Stronger pushdown, Warp Speed integration, extra authentication — Hive · Iceberg · Delta Lake · Oracle · BigQuery · SQL Server · PostgreSQL · MySQL · MongoDB · Kafka · Elasticsearch · Redshift · ClickHouse and more
Identical to Upstream Trino
DuckDB · Druid · Pinot · Prometheus · Redis · OpenSearch · Google Sheets and the rest of the community connectors
Rule of thumb: if "Trino on S3 + PostgreSQL" covers your requirements, OSS is enough. The moment Snowflake, Oracle, Teradata, SAP, or Salesforce appears in your architecture, the scale tips toward SEP.
Apache Iceberg Lakehouse — Beyond the Partition Wall, With Operations Automated
Iceberg is an open table format that turns piles of files on object storage into database-like tables. Hidden partitioning and partition evolution are shared benefits of Trino + Iceberg; SEP automates the operational chores that remain — small files, snapshots, statistics.
Shared Benefits of Trino + Iceberg
- Hidden Partitioning — Partitions are defined as column transforms (day · bucket · truncate) rather than directories — filters on the original column prune automatically, eliminating accidental full scans.
- Partition Evolution — Change partition strategy without rewriting data — a single query transparently handles mixed old/new specs.
- ACID · Time Travel — Snapshot-based transactions, point-in-time reads (FOR VERSION/TIMESTAMP AS OF), schema evolution, and snapshot rollback to recover from bad writes.
- Manifest Pruning — File-level min/max statistics drive pruning — scaling to millions of files without an RDBMS metadata bottleneck.
What SEP Adds — Automated Table Maintenance
- Automatic Compaction — Runs optimize (small-file merging) automatically on a schedule.
- Snapshot & Orphan-File Cleanup — Automates expire_snapshots and remove_orphan_files to prevent metadata bloat.
- Automatic Statistics Collection — Keeps the table statistics used by the CBO up to date.
- Streaming Ingest · File Loader — Managed continuous ingestion from Kafka and more into Iceberg tables — no separate Spark/Flink pipeline required.
You can run the same commands manually or on a schedule in OSS Trino, but the operational responsibility falls on your platform team. SEP delivers this as managed automation — and combined with S3-compatible object storage such as MinIO, it completes the on-premises lakehouse.
Performance — Warp Speed and Benchmarks
Because the engine is the same, numbers match OSS Trino with acceleration off. Meaningful performance differences come from Warp Speed and the architecture shift.
Faster query execution — Warp Speed (TPC-DS SF1000)
CPU time saved — Warp Speed
Query performance vs Hive (TPC-DS · Iceberg)
Cost efficiency vs cloud DWs
Warp Speed Effect — vs Standard Execution
Standard execution = 100, lower is better (TPC-DS SF1000 · Iceberg)
The engine is identical, so with acceleration off the numbers match OSS Trino — the difference is precisely the Warp Speed effect.
Query Execution Time vs Hive
Seconds, lower is better — 1TB source (S3 · Parquet), Starburst 476-e vs Hive 3.1.3
Source: Concurrency Labs benchmark report (2025-09) · TPC-H 9 nodes / TPC-DS 17 nodes (32 vCPU · 128GB each)
Warp Speed Smart Indexing & Caching
Automatically picks the optimal index type per block (bitmap · dictionary · tree) and caches hot data on worker NVMe SSDs — accelerating repetitive, selective workloads with no manual index design.
Up to 22× vs Hive
TPC-DS on Iceberg: 37,901s → 1,737s (10.5 hours → 29 minutes). Swapping the engine alone yields 6–8×; migrating to Iceberg raises it to 12–22× — gains grow step by step, no big bang required.
Price-Performance vs Cloud DWs
On SQL over open table formats, up to 6.3× faster queries and up to 12.7× lower cost — Starburst is the only engine near the bottom on both runtime and cost.
Fault-Tolerant Execution (FTE)
Long-running batch and ETL queries recover automatically instead of failing outright — enterprise-hardened up to 60TB scale.
Performance figures are from Starburst's published benchmarks and vary by workload — we recommend PoC validation before adoption.
Security and Access Control — Table Stakes for Regulated Industries
Authentication is fine in OSS Trino. Where they diverge is fine-grained authorization and audit — the decisive reason for SEP in finance, healthcare, and the public sector.
Built-in Access Control (BIAC)
SEP's native authorization model — role-based (RBAC) and attribute-based (ABAC) permissions at catalog/schema/table/column/row level, managed through a UI.
Column Masking · Row Filtering
Dynamic masking of sensitive columns and row-level filters out of the box — meeting personal-data and transaction-data protection requirements.
Apache Ranger Integration
Global/catalog/column/row level — reuse the same Ranger policies you already run on Hive/HDFS.
Credential Passthrough
Kerberos and password credential passthrough (with caching) — workers access sources under the user's identity without long-lived service accounts.
Authentication Integrations
LDAP · OAuth2 · JWT · Kerberos plus Okta integration, with user impersonation generalized.
Query Audit
A persistent record of who ran what query, when, against which catalog — instantly answering audit-trail requirements.
Practical effect: policies converge in one place (BIAC or Ranger) and apply uniformly across every connector SEP exposes.
Governance & Operations — Value That Shows at Scale
This is where a pure query engine (Trino) and a data platform (SEP) diverge most — in OSS, every item below must be assembled yourself with separate tools.
Data Products
Publish curated datasets with owners, docs, SLOs, and access policies on top of your catalogs — a self-service marketplace for data mesh architectures.
Starburst Insights
Dashboards for query patterns, resource usage, and slow queries plus data lineage and Apache Atlas integration — answering "what was the cluster doing at 3am yesterday?"
HA · Autoscaling
Coordinator high availability, graceful scaledown that drains workers without killing running queries, and cost automation via auto-suspend, idle shutdown, and cluster scheduling.
Quality · Auto-Classification
Data quality rules, schema change alerts, automatic data classification (sensitive-data detection), and unified search across sources.
Stargate
Cluster-to-cluster federation — route queries across regions, tenants, and borders without copying data, meeting data-sovereignty requirements such as GDPR.
Supported Deployment Paths
Kubernetes Helm charts · Red Hat OpenShift · Starburst Admin (Ansible) · AWS/Azure/GCP Marketplace, with CloudWatch/Stackdriver observability integration.
OSS gets you as far as JMX, logs, and the basic UI — the query history store, the query-respecting autoscaler, and the metrics pipeline above that are yours to build. Priced in engineering hours, "build it yourself" rarely wins.
OSS Trino vs Starburst Enterprise
Engine freshness, openness, and cost favor OSS Trino; security, governance, acceleration, operations, and support SLAs favor SEP — the more regulated and the larger the enterprise, the stronger the case for SEP.
| Category | OSS Trino | Starburst Enterprise (SEP) | Verdict |
|---|---|---|---|
| Core query engine | Full Trino engine — latest features first | Same engine, certified & validated + proactive patches/backports | Tie |
| Security · authorization | Manual file/OPA config, no RBAC UI | BIAC (row/column RBAC·ABAC) · masking · Ranger · audit UI | SEP |
| Governance · catalog | Relies on external tools (OpenMetadata etc.) | Data Products · lineage · quality rules · discovery portal | SEP |
| Performance acceleration | CBO · dynamic filtering | + Warp Speed (auto indexing & caching) · workload management | SEP |
| Table maintenance | Schedule optimize etc. yourself | Automated compaction · snapshot expiry · stats collection | SEP |
| Connectors | 50+ community connectors (variable quality) | Certified connectors + exclusives (Snowflake · SAP · Salesforce…) | SEP |
| Deployment · operations | Self-managed Helm | HA/DR · autoscaling · OpenShift · Marketplace | SEP |
| Support | Community Slack · GitHub | 24×7 · 30-min SLA · dedicated TAM · 12-month LTS patches | SEP |
| Cost · license | Free under Apache 2.0 — no vendor lock-in | Commercial subscription (node/compute-based) | OSS |
If "Trino on S3 + PostgreSQL" covers your requirements, OSS is enough — the moment security, governance, or support-SLA requirements appear, evaluate SEP.
Version Policy — LTS / STS
Trino releases roughly every two weeks. SEP adds a predictable release cadence and an LTS patch lifecycle on top, turning it into a pace enterprises can follow. The leading integer always matches the upstream Trino version — a patch suffix (-e.x) means LTS, none means STS.
| Item | LTS | STS |
|---|---|---|
| Support window | 12 months from release | Until the next STS |
| Patch releases | Provided (-e.0 → -e.1 → …) | None (rolled into the next release) |
| Release cadence | Up to one per quarter (Feb·May·Aug·Nov) | Tracks Trino (frequent) |
| Security (CVE) fixes | Backported as patches | Next STS only |
| Production fit | The default for production | Feature-driven · close to upstream |
Recommended playbook
One controlled upgrade per year, LTS to next LTS — target a line with 9–12 months of support remaining and handle breaking changes line by line.
EOL is a hard deadline
No CVE patches after end of support — at any moment roughly four LTS lines overlap in support, giving you an upgrade runway.
The Starburst Product Family
Starburst Enterprise
A self-managed commercial Trino distribution deployed on your own infrastructure (on-prem · private VPC). Retain full control of your data while using enterprise security and operations features — suited to data gravity, network isolation, and data-residency requirements.
Starburst Galaxy
A fully managed SaaS running on AWS, Azure, and GCP. Cluster autoscaling, pay-per-query pricing, and cross-cloud federation let you start analyzing immediately with near-zero operational burden.
For on-premises and private-environment enterprises (especially regulated industries), SEP is the usual starting point; for cloud-first organizations, Galaxy.
Flagship Scenario — Modernizing Legacy Hadoop / Hive Platforms
Slow ad-hoc queries on Hive are not a tuning problem — they are a structural mismatch of using a batch engine interactively. Starburst + Iceberg + object storage removes the mismatch at the architecture level.
Coexist
Connect to the existing Hive metastore and start with reads — zero data movement
Offload
Move heavy ad-hoc/BI queries over — performance felt immediately
Modernize
Convert core tables to Iceberg — maximize the gains
Consolidate
Shrink the legacy footprint; security, governance, and autoscaling settle in
Existing Hive data stays queryable as-is, so you migrate step by step with KPIs and rollback points at each stage — no big bang.
Global Adoption — Led by Financial Services
Real-world migrations and federations from legacy platforms such as Hive/Hadoop, Teradata, and DB2 to Trino-based Starburst.
HDFC Bank
India's largest private bank — from legacy DB2 to an open architecture, running Azure AKS and on-prem OpenShift side by side.
Unified cloud/on-prem governance · lower DB2 spend
Crédit Mutuel Arkéa
France — built a federated query engine over Hadoop while staying on-premises.
10–20× faster Hadoop queries · 550+ users within a year
Bank Hapoalim
Israel — moved 20 years of Teradata to a data lake, choosing Starburst after benchmarking Hive and Spark.
Best performance for high-concurrency ad-hoc + BI
Banco Inter
Brazil — from Amazon Athena to Starburst, unifying discovery, governance, and self-service analytics on one platform.
Insights from days to seconds · $100K saved monthly
Global investment bank
Replaced Hive with Starburst — in-place queries across 100+ countries without data movement, powering AML analytics.
Data-sovereignty compliance · lower regulatory-fine risk
Global banking group (62 countries)
Stargate-based cross-border analytics — GDPR/CCPA compliance with zero data movement.
20× query performance · data-sovereignty compliance
Starburst AI — Extending Toward an AI-Ready Data Platform
A roadmap that connects structured and unstructured data directly to AI workloads on a single governed access point.
AI Search
Store and search structured and unstructured data as vector embeddings in Iceberg — semantic search in the lakehouse without a separate vector DB.
AI SQL Functions
Call LLM functions such as prompt(), classify(), and mask() directly in SQL — analysts use AI in the language they already know.
AI Model Access Management
Model access control, usage monitoring, and audit logs — govern AI models under the same regime as your data.
Maturity caveat: much of the Starburst AI feature set is in preview, and availability varies by SEP version and timing — treat it as "AI-ready foundation + roadmap" rather than immediate production features, and confirm GA status at PoC time.
Services from Data Dynamics
As a Starburst partner we support the full adoption lifecycle — licensing, implementation, operations, and training.
License Sales
We assist with Starburst Enterprise and Galaxy license quotes and orders.
- Capacity/node-based sizing
- PoC license support
- Renewal & expansion consulting
Technical Support
We provide end-to-end technical support for Starburst, from implementation to operations.
- Installation & upgrade support
- Query performance tuning
- Incident response & escalation
Training · PoC
We deliver Trino/Starburst training and tailored PoC engagements.
- Administrator courses
- Data analyst training
- Custom PoC design & execution
We bring hands-on experience building Trino·Iceberg·object-storage lakehouses and migrating from Hive/Impala, and we continuously publish technical material on our blog.