Trino vs Spark: Query Engine or Processing Engine? (2026)

Quick verdict

Short answer

Choose Trino when the job is SQL: analysts and BI tools running interactive queries over a data lake, or joining data across databases and object storage without copying it first. Choose Apache Spark when the job is processing: pipelines written in Python or Scala, DataFrame transformations, streaming from Kafka, or machine learning, as well as SQL. They overlap on batch SQL over Iceberg, Delta Lake and Hive tables, and in that overlap the deciding factors are who writes the code (SQL users or engineers) and whether you need anything beyond SQL.

How we know: This comparison is research-based: architecture, APIs, streaming features, current releases and managed offerings were checked against trino.io, spark.apache.org and the Databricks, AWS and Google Cloud documentation in October 2026. We have not run benchmarks, and no speed or cost-efficiency claims are made.

Trino is an Apache-2.0 licensed distributed SQL query engine, supported by the Trino Software Foundation; it began as PrestoSQL, a fork of Presto (see Trino vs Presto). A coordinator parses and plans each statement and splits it into stages and tasks that workers run over splits of the data, in memory. Trino stores nothing itself: catalogs connect it to object storage table formats (Iceberg, Delta Lake, Hudi, Hive) and to databases. Its documentation is explicit that it "is not a general-purpose relational database" and "was not designed to handle Online Transaction Processing (OLTP)"; it targets analytics. The latest release is Trino 483 (17 July 2026).

Apache Spark is an Apache-2.0 licensed engine from the Apache Software Foundation for large-scale data processing. It offers Spark SQL, DataFrame and Dataset APIs (PySpark, Scala, Java; the R API, SparkR, is deprecated since Spark 4.0), Structured Streaming, MLlib and a pandas API on Spark, and runs on its own cluster manager, YARN or Kubernetes. Spark 4.2.0 (14 July 2026) is the latest feature release, with maintenance lines 4.1.x, 4.0.x and 3.5.x. Spark 4 made ANSI SQL mode the default and requires Java 17 or later and Scala 2.13.

Side by side

Aspect	Trino	Apache Spark
What it is	Distributed SQL query engine	General-purpose distributed processing engine with a SQL module
Interfaces	SQL only, through the CLI, JDBC and client libraries	Spark SQL plus DataFrame APIs in Python, Scala and Java (SparkR is deprecated); Spark Connect clients
Typical workload	Interactive and ad hoc analytics, BI dashboards, federated queries	ETL and ELT pipelines, batch transformation, streaming, machine learning
Federation	Core design: one query joins catalogs across lakes and databases	Possible through data sources such as JDBC, but usually used to load data into the lake
Streaming	No stream processing (a Kafka connector queries topics as tables)	Structured Streaming, including real-time mode since 4.1
Machine learning	None built in	MLlib, and ML over Spark Connect
Long-running batch jobs	Fault-tolerant execution must be enabled (QUERY or TASK retry policy)	Task retry and lineage-based recovery are part of the engine
Managed options	Starburst Galaxy and Enterprise; Trino on Amazon EMR; Athena engine version 3 is Trino-derived	Databricks; Amazon EMR; Google Cloud Managed Service for Apache Spark (formerly Dataproc); others
Main trade-off	Simple for SQL users, but SQL only and not a processing framework	Covers almost every data job, but more to learn and operate for pure SQL querying

Key differences

A query engine versus a processing framework

Trino does one thing: it accepts a SQL statement, plans it on the coordinator, and executes it across workers that read from connectors. There is no programming API beyond SQL, no stream processing and no machine learning library. That narrow scope is the point: analysts, BI tools and notebooks connect through JDBC or a client library and get a single SQL dialect over every catalog.

Spark is a framework for writing data applications. SQL is one way in, but much Spark code is PySpark or Scala that reads data, transforms it with DataFrame operations, calls Python functions, and writes tables. Spark 4.1 added Spark Declarative Pipelines, a framework where you define datasets and queries and Spark handles dependency ordering, checkpoints and retries, and made SQL Scripting generally available. Spark 4.2 adds a SQL CHANGES clause and Auto CDC in Declarative Pipelines. In our view, if the people doing the work write SQL and want answers, Trino is the more direct tool; if they build pipelines in code, Spark is.

The same question, two ways

A Trino query can join a lakehouse table with a live operational database through two catalogs:

-- Trino SQL
SELECT o.order_date, count(*) AS orders, sum(o.total) AS revenue
FROM iceberg.sales.orders AS o
JOIN mysql.crm.customers AS c ON c.id = o.customer_id
WHERE c.country = 'GB'
GROUP BY o.order_date
ORDER BY o.order_date;

In Spark the same logic is often written as a pipeline step in PySpark that reads the operational table over JDBC and writes the result as a new table for others to query:

# PySpark
from pyspark.sql import functions as F

customers = (spark.read.format("jdbc")
    .option("url", "jdbc:mysql://crm-db:3306/crm")
    .option("dbtable", "customers")
    .option("user", "reader").option("password", "...")
    .load())
orders = spark.read.table("sales.orders")

daily = (orders.join(customers.filter(F.col("country") == "GB"),
                     orders.customer_id == customers.id)
         .groupBy("order_date")
         .agg(F.count("*").alias("orders"), F.sum("total").alias("revenue"))
         .orderBy("order_date"))
daily.write.mode("overwrite").saveAsTable("reporting.daily_gb_revenue")

Spark SQL could express the query as SQL too, and Spark's Thrift JDBC/ODBC server and spark-sql CLI give SQL access. The difference is emphasis: Trino is built for federated SQL on demand; Spark for producing data that is then queried, by Spark or by an engine such as Trino.

Streaming and machine learning

Spark's Structured Streaming treats a stream as an unbounded table, using the same DataFrame and SQL APIs as batch. Spark 4.0 added version 2 of the arbitrary state API and a state data source for debugging, and Spark 4.1 introduced real-time mode, which the release notes describe as continuous, sub-second latency processing. MLlib provides machine learning algorithms, and Spark 4 supports ML over Spark Connect.

# PySpark Structured Streaming: count Kafka events per minute
events = (spark.readStream.format("kafka")
    .option("kafka.bootstrap.servers", "broker:9092")
    .option("subscribe", "clicks")
    .load())
counts = events.groupBy(F.window("timestamp", "1 minute")).count()
query = counts.writeStream.outputMode("update").format("console").start()

Trino has neither. Its Kafka connector lets you query messages in a topic as a table, which is useful for inspection, but it does not run continuous queries. If streaming or ML is part of the requirement, Spark (or another stream processor) is needed regardless of which engine serves SQL.

Reliability for long jobs

Trino was designed for queries that finish quickly; by default, a worker failure fails the query. Its documented fault-tolerant execution adds a QUERY retry policy and a TASK policy that spools intermediate data to an exchange manager on S3, Azure, GCS, HDFS or local storage, aimed at long-running batch queries. Spark re-runs failed tasks and recomputes lost partitions as a basic part of its engine, which is one reason it is the common choice for multi-hour transformation jobs. Neither project publishes figures we can rely on here, so test your own workloads.

Managed services and versions

For Trino, Starburst sells Starburst Galaxy (SaaS on AWS, GCP and Azure) and Starburst Enterprise; Amazon EMR ships Trino; and Amazon Athena engine version 3 is AWS's own engine that incorporates Trino functionality.

For Spark, Databricks is built around it: Databricks Runtime 19 (June 2026) includes Apache Spark 4.2.0. Amazon EMR 7.14.0 ships Spark 3.5.8, so EMR users are not yet on Spark 4. Google Cloud now documents its service as Managed Service for Apache Spark (formerly Dataproc). Spark versions on managed services can lag the open source release, so check which features you depend on. For platform choices, see Snowflake vs Databricks and the best Databricks alternatives.

Pricing and licensing

Trino and Apache Spark are both free and open source under the Apache License 2.0. Self-managed, you pay for the compute and storage they run on and the effort of operating clusters.

Managed services price differently. Databricks bills Databricks Units (DBUs) by compute type, on top of the cloud provider's infrastructure charges in its classic model. Amazon EMR adds an EMR charge to the underlying EC2 or other compute. Amazon Athena charges per TB of data scanned. Starburst offered a Starburst Galaxy free trial with up to USD 500 in credits in October 2026, with Starburst Enterprise sold by quote. We have not quoted per-unit figures, because they vary by region, tier and compute type; use each provider's pricing calculator.

Pricing checked on the vendors' official pages on 7 October 2026. Prices change; confirm before buying.

Where each one leads

Trino strengths

One SQL dialect over many sources, joined in a single query without copying data
Suited to interactive and BI queries by SQL users through JDBC and client libraries
Simple model to learn: catalogs, schemas, tables and SQL
Broad connector list, including Iceberg, Delta Lake, Hudi, Hive and many databases

Apache Spark strengths

Python, Scala and Java APIs alongside SQL, for pipelines written as code
Structured Streaming, including real-time mode since Spark 4.1
MLlib and pandas API on Spark for machine learning and data science work
Engine-level task retries suit long batch jobs
Widest managed availability, from Databricks to EMR and Google Cloud

Limitations

Trino limitations

SQL only: no DataFrame API, stream processing or machine learning
Fault tolerance for long jobs must be configured, with an exchange manager for task retries
Not a database: storage, catalogs and table maintenance are separate systems
Frequent releases mean regular upgrade work when self-managed

Apache Spark limitations

More to learn and tune than a SQL engine when all you need is querying
Federated queries are possible but are not its main design goal
Managed services can lag the latest release (EMR 7.14.0 ships Spark 3.5.8)
Spark 4 changes such as ANSI SQL mode by default can change how older SQL behaves; test upgrades

When to choose each

Choose Trino if

Analysts and BI tools need interactive SQL over a data lake
You need to join data across databases and object storage without building a pipeline first
Your team works in SQL rather than Python or Scala
You already produce Iceberg or Delta tables and want a separate engine to serve queries

Choose Apache Spark if

You build ETL or ELT pipelines in Python or Scala
You need stream processing from Kafka or similar sources
Machine learning or data science runs on the same data
Jobs run for hours and must tolerate worker failures without extra setup
You want a lakehouse platform such as Databricks built around the engine

When neither is right

You want a managed warehouse that stores data and handles concurrency for you: see Snowflake vs BigQuery and Data Warehouse vs Database.
Your data fits on one machine: DuckDB is simpler; see DuckDB vs pandas and DuckDB vs Snowflake.
You need low-latency analytics served to applications: see ClickHouse vs Databricks.
You mainly need to move data from SaaS apps and databases into a warehouse: an ingestion tool fits better; see the best ETL tools.

Final recommendation

Bottom line

Trino and Spark are less rivals than neighbours. Trino is the better fit when the work is SQL questions over data that lives in many places, asked by people and BI tools who want answers quickly and do not want to write code. Apache Spark is the better fit when the work is building data: pipelines, streams and models written in Python or Scala as well as SQL. In a lakehouse, a common pattern is Spark writing Iceberg or Delta tables and Trino querying them; pick one alone only if your workload sits clearly on one side.

Frequently asked questions

Is Trino faster than Spark?

We do not make speed claims. The engines are designed for different jobs: Trino for interactive SQL, Spark for general processing including long batch jobs. Vendor and community benchmarks exist but are not independently verified here; test your own queries on your own data.

Can Trino replace Spark?

Only for SQL work. Trino has no DataFrame API, no stream processing and no machine learning library. If your Spark jobs are pure SQL over lake tables, Trino may cover them; if they use PySpark transformations, streaming or MLlib, it cannot.

What is the current version of Apache Spark?

Spark 4.2.0, released on 14 July 2026, with maintenance releases 4.1.3, 4.0.4 and 3.5.9 in July 2026. Spark 4 requires Java 17 or later and is built with Scala 2.13.

Can Trino and Spark share the same tables?

Yes, through open table formats and a shared catalog. Both document support for Apache Iceberg, Delta Lake and Hive tables, so Spark can write tables that Trino queries. Check each engine's connector documentation for the table features and versions it supports.

Is Spark SQL the same as Trino SQL?

No. Both follow ANSI SQL closely (Spark 4 enables ANSI mode by default), but functions, types and some syntax differ, so queries may need changes when moved between them.

Sources

Checked October 2026.

How we research comparisons: our editorial method.