Trino or StarRocks: which fits your decision
If your team has both Trino and StarRocks shortlisted, the choice turns on where your data already lives and what your users ask of it. Choose Trino when the main job is querying data in place across many systems, such as lakehouse tables in Iceberg or Delta Lake alongside PostgreSQL, MySQL or Snowflake. Trino is a query engine with no storage of its own: its connectors read from each source, and a Trino lakehouse still needs separate object storage and a table catalog such as an Iceberg REST catalog or Hive Metastore. Its 37 connectors, including Snowflake and PostgreSQL, make it the stronger fit when federation across sources is the requirement and copying data into a new store is not acceptable.
Choose StarRocks when a defined set of analytical data must answer many concurrent dashboard queries with low latency. StarRocks is an MPP database that can run shared-nothing, where backend nodes both store and compute, or shared-data, where stateless compute nodes cache data held in object storage. Its tiered cache and materialized views, which the README says refresh during import and are selected automatically at query time, are built for that dashboard pattern. It also speaks the MySQL protocol, so existing BI tools can connect. The trade-off is that data loaded into StarRocks sits under its own storage model, and you must plan for that data movement.
The decision turns on three questions. First, where the data lives: if most of it stays in other systems that you do not want to copy, Trino's connector model fits; if you can load a curated set into one analytical store, StarRocks fits. Second, the concurrency profile: many simultaneous short dashboard queries favor StarRocks's caching and materialized views, while ad hoc, cross-source exploration favors Trino. Third, operating capacity: StarRocks adds FE and BE or CN nodes, storage and cache tuning to your runbook, and Trino adds coordinator, worker and catalog work. Confirm with each vendor how your versions handle upgrades and cache sizing before you commit.
Which platform fits which scenario
This table compares Trino and StarRocks across the workloads most teams weigh first, naming the better fit and the condition that decides it. Read each row against your own sources, concurrency needs and staffing, since the right choice often depends on whether data stays in place or moves into a managed store.
| Scenario | Better fit | Condition that decides it |
|---|---|---|
| Ad hoc analysis across many systems | Trino | Trino has connectors for many systems, including PostgreSQL, MySQL, Oracle, SQL Server, MongoDB, Snowflake and BigQuery, among 37 connectors in total. Choose it when the questions span sources that should stay where they are. |
| Lakehouse queried by several engines | Trino | Trino's Iceberg connector works with Hive Metastore, AWS Glue, REST and Nessie catalogs and reads and writes Parquet, ORC and Avro. It fits when Spark, Flink or other engines share the same tables and object storage. |
| High-concurrency dashboards on curated data | StarRocks | StarRocks stores data itself or caches hot data on compute nodes, supports a primary key model with upserts and deletes, and uses materialized views selected at query time. It suits dashboards that repeat the same shapes of query at scale. |
| Dashboards served through a MySQL-compatible BI tool | StarRocks | StarRocks is compatible with the MySQL protocol, so MySQL clients and BI tools can connect. Confirm that your BI tool's driver and query features behave as expected before committing. |
| Interactive queries on live source systems | Trino | Trino reads source systems through connectors and does not copy them into its own storage. Choose it when freshness at the source matters more than sub-second dashboard latency. |
| One database to run for analytics and serving | StarRocks | StarRocks combines storage, compute and materialized views in one system. Choose it when the team wants one engine to own ingestion, storage and query serving. |
| Long batch queries that must survive worker failures | Trino | Trino's fault-tolerant execution is off by default and TASK retry needs an exchange manager spooling to S3, Azure Blob, GCS or HDFS. Confirm this setup works for your batch sizes before relying on it. |
| Lakehouse with materialized views on Iceberg tables | Confirm with both vendors | StarRocks can query Iceberg directly and selects materialized views at query time, while Trino's Iceberg connector covers time travel and MERGE. Test your own view definitions and refresh behavior on both. |
How Trino and StarRocks are built and deployed
Trino and StarRocks both run as clusters of several server roles, but they divide storage and compute differently. The choice between them starts with where your data must stay and who will operate the cluster after go-live.
Trino coordinator and workers
Trino runs one coordinator that plans queries and workers that execute them, and the docs recommend a dedicated coordinator for larger clusters. It holds no data of its own. Each worker reads through connectors, so scaling means adding workers, and a lost worker affects in-flight queries unless fault-tolerant execution is enabled. Check how your team will size workers against the connectors and concurrency it needs.
Separate storage and catalog for Trino
A Trino lakehouse keeps data in object storage you run and tracks tables in a catalog such as an Iceberg REST catalog or Hive Metastore. Trino does not load data into its own storage, so you operate the object store, the catalog and the cluster as three components. Upgrades of Trino do not move data, but catalog and table format compatibility must be checked on each change.
StarRocks frontend and backend nodes
In shared-nothing mode, frontend (FE) nodes manage metadata, client connections, query planning and scheduling, and run as leader, follower or observer. Backend (BE) nodes both store data and compute, so adding capacity usually means adding nodes that hold data. Plan for rebalancing and node replacement as part of routine operations, and confirm the recovery steps with StarRocks for your version.
Stateless compute nodes in shared-data mode
In shared-data mode, stateless compute nodes (CNs) run queries and keep a cache while the data lives in object storage such as S3, GCS, Azure Blob Storage, MinIO or HDFS. A tiered cache across memory, local disk and remote storage serves hot data, and cold data loads from object storage. Compute can be added or removed without moving data, but cache warm-up and object storage latency become part of performance planning.
Kubernetes routes for both engines
Trino documents running on Kubernetes with Helm, and StarRocks maintains an Apache-2.0 Kubernetes operator in its own repository. Both routes help standardize rollout and restarts, but neither removes the work of sizing nodes, storage classes and network policy. Ask each vendor or your platform team which chart or operator version you will pin, and how upgrades are rehearsed before production.
Failure handling and upgrade risk
Trino's default standard execution fails a query when a worker fails, and fault-tolerant execution with a QUERY or TASK retry policy must be enabled explicitly. TASK retries need an exchange manager spooling to object storage or HDFS. StarRocks failure handling depends on whether data sits on BE nodes or in shared storage. Test node loss, coordinator or FE failover and rolling upgrades in your own environment before you commit.
Capabilities compared on supplied facts
This table compares Trino and StarRocks on the capabilities that decide fit: licensing, query model, storage ownership, caching, MySQL compatibility, upserts, fault tolerance, access control and Kubernetes support. Where the supplied facts do not settle a point, the cell tells you what to test before you commit. Read it as a map of where each engine's design starts, then check the gaps against your own workload.
| Capability | Trino | StarRocks |
|---|---|---|
| License | Apache-2.0 (trinodb/trino repository) | Apache-2.0, described as a Linux Foundation project |
| Query model | Distributed SQL engine with a coordinator and workers that query sources in place through connectors | MPP analytics database with frontend and backend or compute nodes that plan and execute queries |
| External sources | 37 connectors, including Iceberg, Delta Lake, Hive, Hudi, PostgreSQL, MySQL, Snowflake and BigQuery | Queries Hive, Iceberg, Delta Lake and Hudi directly without import; confirm connector coverage for your other sources |
| Storage ownership | Owns no data; needs separate object storage and a table catalog such as an Iceberg REST catalog or Hive Metastore | Shared-nothing storage on backend nodes, or shared-data storage in S3, GCS, Azure Blob, MinIO or HDFS |
| Materialized views | Not established in the supplied facts; confirm the feature set for your connectors | Refresh during data import and are selected automatically at query time |
| Caching | Not established in the supplied facts; confirm caching behavior with your connectors and storage | Tiered cache across memory, local disk and remote storage in shared-data mode |
| MySQL protocol compatibility | Not established in the supplied facts; confirm client and BI tool compatibility before relying on it | Compatible with the MySQL protocol, so MySQL clients and BI tools can connect |
| Primary key upserts and deletes | Iceberg connector supports MERGE, UPDATE and DELETE on Iceberg tables; confirm behavior on other connectors | Primary key model with upserts and deletes inside StarRocks tables |
| Fault-tolerant execution | Off by default; QUERY or TASK retry policies, with TASK needing an exchange manager spooling to S3, Azure Blob, GCS or HDFS | Not established in the supplied facts; confirm recovery behavior for long-running queries with the vendor |
| Authentication and access control | Password file, LDAP, OAuth 2.0, Kerberos, certificates, JWT; file-based access control, Apache Ranger or Open Policy Agent | Not established in the supplied facts; confirm authentication and tenant isolation options, including resource management |
| Kubernetes deployment | Documented Helm-based guide for running on Kubernetes | Apache-2.0 Kubernetes operator maintained in the StarRocks Kubernetes operator repository |
What each license and edition means for your team
Trino is licensed under Apache-2.0, and its README describes it as a fast distributed SQL query engine for big data analytics, formerly known as PrestoSQL. For your team, that means you can run, modify and redistribute the engine inside your own infrastructure under the terms of that license, without a license fee for the open-source project itself. The license governs the code. It does not decide who supports your cluster, who ships security fixes on a schedule you can plan around, or whether a vendor sells a managed or enterprise variant, so those questions sit outside the license and need separate answers.
- License terms: Apache-2.0 allows commercial use, modification and internal redistribution, with attribution and notice obligations that your legal team should review against your own use.
- Release cadence: Release 482 was published on June 25, 2026, release 483 on July 17, 2026 and release 484 on October 15, 2026, so plan upgrades around a frequent release train.
- Governance: The project is maintained in the trinodb/trino repository, and your team should review its contributor base and release process before treating it as a long-term dependency.
- Support model: No supplied fact describes a paid Trino edition or commercial support tier, so any support you need must be confirmed with a named vendor or distributor.
Before committing, ask any commercial Trino provider for current support terms, indemnities and upgrade commitments in writing, and confirm them against the release notes.
StarRocks is licensed under the Apache License 2.0 and describes itself as a Linux Foundation project, according to its GitHub README. Linux Foundation stewardship generally means the project sits under a neutral governance structure rather than a single company's roadmap, which matters if you expect to depend on it for several years. The supplied facts do not describe the foundation's specific governance documents, the maintainers' roles or any commercial companion product, so you should read the project's governance files directly before drawing conclusions about control.
- License terms: Apache-2.0 applies to the StarRocks codebase, so you can self-host and modify it in your infrastructure under the same permissive terms as Trino.
- Foundation status: The README describes StarRocks as a Linux Foundation project, which is a governance signal to verify in the project's own documentation rather than an assumption about future direction.
- Release line: StarRocks 4.1.3 was published on September 17, 2026, so check the release notes for the version you intend to run.
- Commercial offerings: No supplied fact describes paid editions, managed services or support tiers, so confirm any commercial option with its provider in writing.
Before relying on StarRocks for production, ask any commercial provider for written terms on support windows, upgrade paths and security patch timing.
The supplied facts describe the open-source projects only. They do not state that either Trino or StarRocks has a paid edition, an enterprise license or a commercial support tier. That absence is not proof that none exists, because commercial distributors and managed services may be offered separately. Your procurement and architecture reviews should treat this as an open question with a clear owner and a written answer, not as a settled fact in either direction.
- What to ask for: Request current edition names, feature differences, support hours, response targets and security patch commitments in writing from each vendor or distributor.
- Who is paid for what: Open-source use carries no license fee, but you will pay for infrastructure, staff time and any commercial support contract, so separate those lines in your cost model.
- Recurring versus one-time: Support and managed service fees usually recur, while integration and migration work is typically one-time, so ask vendors to split quotes by these categories.
- Version commitments: Ask which release lines receive fixes and for how long, and whether any commercial terms change the upgrade path.
- Reproducible evaluation: Test any commercial claim against your own deployment and the public release notes before accepting it.
Before you sign anything, confirm which terms apply to your exact deployment model and the version you plan to run.
How dashboard concurrency and caching change the answer
Dashboards with many simultaneous users and repeated queries favor a system that stores and indexes data for serving, and this is the pattern StarRocks is built for. Its materialized views, which refresh during data import and are selected automatically at query time, let many users hit precomputed results instead of rescanning raw files for every chart refresh. Its primary key model supports upserts and deletes, so operational or change-heavy tables can stay current for the dashboard. In shared-data deployments, the tiered cache across memory, local disk and remote object storage keeps hot data close to the compute nodes. This advantage matters most when a fixed set of dashboards runs frequently against the same tables and users expect sub-second refreshes. Confirm the concurrency limits, resource group settings and cache sizing for your own workload with StarRocks before you size a cluster.
Trino's strength runs in the other direction. Each query is planned and executed across connectors, so one statement can join an Iceberg table, a PostgreSQL table and a Snowflake table without first copying them into a single store. That breadth of sources is the reason to choose it, and it comes with a trade-off: Trino does not hold its own copy of the data to serve repeated dashboard reads, so latency depends on the underlying sources and any caching you configure around them. Its fault-tolerant execution targets a different problem, long batch queries that would otherwise fail when a worker is lost. The TASK retry policy needs an exchange manager spooling to S3, Azure Blob Storage, Google Cloud Storage or HDFS, which is a setup decision you take on if your queries run for hours rather than seconds.
Benchmark both with your own query mix before deciding. Capture the real dashboard queries, their concurrency at peak, the freshness each team needs and the batch jobs that run alongside them, then run that set against a test copy of the data on each platform. Record p95 latency under concurrent load, the effect of a cold cache, the cost of the first query after a refresh and how often a batch query fails and restarts. If most of your load is a known set of dashboards on a few large tables, the serving design of StarRocks is the stronger test case. If most of your load is ad hoc exploration across many sources, with long-running queries, Trino deserves the heavier weighting. Ask both vendors and your platform team to confirm the results on your hardware, not on published figures.
What drives the running cost of each platform
Trino and StarRocks both cost money in compute, storage and the people who run them, but the mix differs. Trino leaves data in place and pushes work to the systems it queries, while StarRocks asks you to size and operate its own compute and, in shared-data mode, its own cache layer. The cards below separate one-time setup from recurring spend so you can ask for a quote that shows both.
Compute for query clusters
Trino runs one coordinator plus workers, and the docs recommend a dedicated coordinator for larger clusters. StarRocks splits frontend (FE) nodes from backend (BE) nodes in shared-nothing deployments, or from stateless compute nodes (CNs) in shared-data deployments. Compute is usually the largest recurring line for both, and it scales with concurrency and query size. Ask the quote to state node counts, sizing assumptions and whether coordinators, FE nodes and CNs are priced separately from workers.
Object storage and data copies
A Trino lakehouse reads data where it sits, so the object storage bill belongs to your existing lake and is not a Trino product cost. StarRocks shared-data mode keeps data in object storage such as S3, GCS, Azure Blob Storage or MinIO, while shared-nothing mode stores data on its own backends. Ask whether the plan copies data into StarRocks storage, how many replicas it keeps and what egress or request charges your cloud account will bill.
Catalog and spooling infrastructure
A Trino Iceberg setup also needs a table catalog such as an Iceberg REST catalog, Hive Metastore or AWS Glue, and it needs a catalog service that someone patches and backs up. Fault-tolerant execution with the TASK policy adds an exchange manager that spools to S3, Azure Blob Storage, Google Cloud Storage or HDFS. Quotes should list each catalog, spool location and the team that owns its availability.
Skills and on-call effort
Running either engine means someone understands query planning, resource groups, cache sizing and connector behavior, and someone is on call when a dashboard slows down. Trino's cost is spread across more connectors and access control choices, while StarRocks concentrates work in its own cluster lifecycle and materialized view maintenance. Ask for the named roles in the scope and for the hours assumed for on-call and tuning each month.
Upgrades and release tracking
Trino ships numbered releases, and release 484 was published on October 15, 2026, so an upgrade plan has to track a steady cadence. StarRocks 4.1.3 was published on September 17, 2026, and both projects change configuration and connector behavior between versions. Recurring upgrade work includes staging tests against your real catalogs, rolling cluster restarts and regression checks on dashboards. Ask the quote to state how many upgrade cycles per year it covers.
Design, migration and paid support
Design, initial tuning, data migration and access control setup are mostly one-time costs, though they can recur when workloads change. Both projects are Apache-2.0, so neither license fee applies, but managed or commercial offerings from third parties and any paid support contract are separate lines. Quotes should separate one-time design and migration from recurring retainers, and they should name any commercial edition or support tier they assume.
Moving between Trino and StarRocks later
This sequence shows how a team moves queries, dashboards and data from one platform to the other without losing track of what changed. It produces a tested cutover in which open table formats carry the data and everything platform-specific is rebuilt and checked.
Inventory queries, dashboards and consumers
Start by listing every SQL query, scheduled job, dashboard, saved chart and downstream application that reads the platform, along with the owner of each. Note which sources each one touches, whether it uses StarRocks-only features such as its primary key model or materialized views, and whether it depends on Trino connectors for systems outside the lakehouse. The step is done when every consumer has an owner and a status of portable, portable with rewrite, or platform-bound.
Decide which data stays in open formats
Sort each dataset by where it should live after the move. Tables already stored as Iceberg, Hive-managed tables, Delta Lake or Hudi can stay in object storage and be read by either engine, provided the catalog is reachable from both. Data that exists only in StarRocks native storage needs an export path to an open format before it can be queried elsewhere. The output is a written list that separates shared open-format tables from tables that must be migrated, and names the catalog each one uses.
Migrate native tables to open formats
For any dataset held in StarRocks internal storage that must remain available to Trino, load it into an open table format in object storage and register it in the chosen catalog. Confirm the schema, partitioning and data types with the platform documentation for both engines, since type mappings can differ. Keep row counts and checksums for each table so the next step has a baseline. The step is complete when each migrated table reads correctly from the target engine.
Rebuild materialized views and caches
Materialized views, cache configuration and warm-up behavior do not move with the data. On StarRocks, recreate materialized views against the target tables and confirm they refresh as expected during import. On Trino, decide which repeated workloads need a different answer, such as a pre-aggregated table refreshed by a pipeline, and build it there. Measure the dashboards that depended on these objects before cutover, not after. Done means each rebuilt object has a documented refresh path and a recorded baseline.
Rebuild access policies and cluster configuration
Access control, authentication and cluster settings are rebuilt, not copied. On Trino, map existing rules to file-based access control, Apache Ranger or Open Policy Agent, and confirm the authentication method you need, such as LDAP, OAuth 2.0 or certificates. On StarRocks, recreate user roles and resource groups that isolate tenants. Compare the policy outcome for a sample of users on both platforms. The step is done when an access review confirms that each role can see the same datasets it could before.
Repoint BI connections
Update each BI tool and application to the new endpoint. StarRocks accepts MySQL-protocol clients, so tools that already use a MySQL driver usually need only a host and credential change. Trino connections use its own JDBC driver and a catalog and schema path, so dashboards may need the connection and dataset references updated. Test one tool at a time in a non-production workspace. The step is complete when each dashboard loads with the expected filters and the owner signs off.
Validate results and run in parallel
Run the inventoried queries on both platforms against the same data and compare outputs, including row counts, aggregates and null handling. Investigate every difference, because SQL dialect and function behavior can vary between engines. Run the two platforms in parallel for a period agreed by the team, then switch consumers over in groups. The output is a signed validation record for each consumer, plus a rollback plan that names the endpoint to revert to.
Retire the old platform and document the result
After the parallel period and sign-off, stop scheduled jobs on the old platform and archive its configuration, schemas and access rules. Keep the inventory, validation records and the catalog entries as the record of the move. Confirm with your compliance owner which logs and audit evidence must be retained from the old system. The step is done when no consumer points at the retired endpoint and the archived artifacts are stored where the team can find them.
What implementing each platform involves and where projects run long
Trino's setup work starts with a cluster of one coordinator and one or more workers, and the Trino deployment documentation recommends a dedicated coordinator for larger clusters. Sizing is driven by the query mix, the number of concurrent users and the memory each query needs, so run representative joins before you commit to node counts. Each source needs its own catalog properties file, and each Iceberg catalog (Hive Metastore, AWS Glue, a REST catalog or Nessie) adds a separate dependency you must run and back up. Authentication (LDAP, OAuth 2.0, Kerberos, certificates or JWT) and access control (file-based, Apache Ranger or Open Policy Agent) are decisions you make early, because retrofitting them across many catalogs is slow. If batch ETL runs on Trino, plan fault-tolerant execution separately: the TASK retry policy needs an exchange manager spooling to object storage, which is another component to size and secure.
StarRocks setup work centers on node roles and the storage mode. Frontend (FE) nodes handle metadata, connections, planning and scheduling and run as leader, follower or observer. In shared-nothing mode, backend (BE) nodes both store and compute data, so capacity planning is tied to disk. In shared-data mode, stateless compute nodes (CNs) run queries and cache data while object storage holds the data, which moves sizing questions to cache capacity across memory and local disk. Confirm the cache size against your hot working set, because cold reads from object storage are slower. Table design (primary key or other table models, partitioning and bucketing) and materialized view maintenance are ongoing work, since views refresh during data import and must be checked against dashboard latency. Ask the vendor or your own tests which storage mode fits your ingestion pattern.
Projects usually run long in three places. Identity integration takes longer than expected in both platforms, because each catalog, BI client and admin path needs its own credentials and group mapping. Data type mapping across sources is the second risk: Trino's connectors translate types per source, and mismatches (decimals, timestamps with time zones, nested fields) surface only when real queries run. The third is performance testing against real dashboards. Concurrency for many dashboards behaves differently from a single analyst's ad hoc query, so test with the actual refresh schedule and user count. Set aside time to tune resource groups in Trino or resource management in StarRocks, and confirm the scope of that work in a written statement before implementation starts.
Questions buyers ask before choosing Trino or StarRocks
Which platform costs less to run, Trino or StarRocks?
Neither license carries a fee: Trino and StarRocks are both Apache-2.0, so the running cost is infrastructure plus the people who operate them. Trino's cost is driven mainly by compute workers, object storage and the table catalog you already run, plus the spill or exchange storage if you enable fault-tolerant execution. StarRocks adds its own storage and cache tiers on top of compute, so you pay for disk and object storage it manages, and for nodes sized for concurrent dashboards. Ask any quote to split infrastructure, licensing (none for either open-source core), and labor, and to state which workloads each sizing assumes.
How much operating effort does each platform need?
Trino is a stateless query layer over data that stays in your storage, so its operating work centers on the coordinator, worker pools, connector configuration, authentication and access control such as file-based rules, Apache Ranger or Open Policy Agent. StarRocks adds stateful work: frontend and backend or compute nodes, data placement, the cache tiers in shared-data mode, and materialized view refresh. Both run on Kubernetes through maintained tooling, and StarRocks publishes an Apache-2.0 Kubernetes operator. Confirm the upgrade path, backup approach for StarRocks-managed data, and monitoring you will need for each.
Can a small team operate Trino or StarRocks?
A small team can run either one, but the staffing question depends on the workload more than on the product. A Trino deployment that only federates a few sources and reads an existing Iceberg or Hive catalog has fewer stateful parts to watch. A StarRocks deployment serving many dashboard users usually needs someone who understands table design, data loading, materialized views and resource groups. Before committing, list the on-call tasks, name who owns each one, and confirm that two people can cover upgrades and incidents without the other.
How long does an implementation of Trino or StarRocks take?
No supplied duration exists for either platform, so the timeline depends on your scope. The main drivers are the number of sources and connectors, the table catalog and object storage you must integrate, identity and access control requirements, whether you need fault-tolerant execution, how many dashboards or users must be validated, and how much data modeling or migration work is needed. Ask for a timeline tied to a written scope with named deliverables. Most DeployOpen engagements are fixed-scope, so the agreed scope, not a generic estimate, sets the schedule.
What skills and time does the work require from our team?
Trino work leans on SQL, connector configuration, Java runtime operations on a supported release, object storage and table catalog administration, and security integration. StarRocks work leans on SQL and MySQL-protocol tooling, schema and table design for its primary key model, materialized view planning, storage tiers and node operations. Both need a team member who can read query plans and debug performance. Time needs differ by phase: design and integration are heavier at the start, and upgrades, access reviews and capacity planning recur. Ask your partner to document the runbook and hand over what your team must own.
Can a Trino lakehouse move to StarRocks later?
Yes, the move is possible because StarRocks can query Iceberg, Hive, Delta Lake and Hudi tables directly, so you can test StarRocks against the same tables Trino reads before changing anything. Where the move gets harder is when you rely on Trino connectors to federate sources that StarRocks does not query, or when you copy data into StarRocks-managed storage. Document every catalog, access rule and dependent dashboard, then run both engines against the same tables for a period to compare results and cost. Confirm table format versions and catalog compatibility for your specific Iceberg setup.
Do we need an outside partner to implement either one?
You do not need a partner, but an outside team can shorten the path if your staff lack time or experience with distributed query engines. DeployOpen plans, implements, integrates, hardens, backs up, upgrades, documents and supports self-hosted open-source platforms inside your own infrastructure, and most engagements are fixed-scope with scope, deliverables and timelines agreed in writing before work starts. DeployOpen does not publish a price list, so a quote follows from the written scope. Ask any partner which parts of the work they will hand over to your team and how ongoing support is structured.
How DeployOpen helps you choose and implement
DeployOpen plans, implements, integrates, hardens, documents and supports self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks. For Trino vs StarRocks, that means first deciding which engine fits your query patterns, then building and handing over the environment you will run.
How we scope
- Map which sources must be queried in place and which workloads need dashboard concurrency or local storage.
- Record the written scope, deliverables and timelines for Trino or StarRocks before any work starts.
- Identify the table catalog, object storage, identity provider and access control model your environment already uses.
How we build
- Deploy the chosen engine in your infrastructure, including Kubernetes or bare metal topology where your standards require it.
- Connect authentication, catalogs and the BI tools your analysts already use, then test representative queries under load.
- Harden configuration, document cache, memory and access control settings, and run security testing against the deployed cluster.
How we hand over
- Deliver runbooks covering upgrades, scaling, backups and failure recovery for the engine you selected.
- Provide documentation and compliance evidence your assessors or internal reviewers can use, without issuing any attestation.
- Walk your operations team through the running system so they can operate it without us on day one.
How we support
- Ongoing support, upgrades and continuous testing are available as a retainer after handover.
- Plan version upgrades against the release cadence of your chosen engine, confirming each change in your own environment first.
- Revisit the choice if workloads shift, for example when dashboard concurrency grows or new sources join the lakehouse.
