Presto vs Dremio

Presto vs Dremio? Which is better for you?

As organizations increasingly shift toward modern data lake architectures, the demand for fast, scalable, and cost-effective query engines has grown exponentially.

Data engineers and analysts now require tools that can handle interactive queries across vast, distributed datasets without compromising on performance or flexibility.

Two leading contenders in this space are Presto and Dremio.

Both are open-source SQL query engines designed for high-performance analytics on data lakes, but they differ significantly in architecture, optimization capabilities, and integration with modern data ecosystems.

• Presto, originally developed at Facebook (now Meta), has become a popular choice for interactive analytics at scale and is used by companies like Uber, Netflix, and Airbnb.

• Dremio, on the other hand, markets itself as a “data lakehouse” engine, offering a user-friendly interface and advanced optimization techniques like Apache Arrow and reflections for accelerated performance.

In this blog post, we’ll compare Presto vs Dremio across several dimensions—including performance, architecture, ease of use, ecosystem integration, and cost considerations—to help you decide which engine best suits your analytics stack.

If you’re also evaluating other query engines or databases, check out our related comparisons like Presto vs Druid and Druid vs Pinot for broader context.

For a deeper understanding of the broader data visualization landscape that often accompanies these engines, you might also be interested in Superset vs Power BI or Superset vs Metabase.

📘 For an in-depth look at how tools like Apache Arrow are shaping modern analytics, check out the Apache Arrow documentation.

📊 Learn more about the data lakehouse concept from Dremio’s official resource center.

What is Presto?

Presto is an open-source, distributed SQL query engine designed for fast, interactive analytics on large datasets.

It was originally developed at Facebook to enable analysts to run complex SQL queries across massive data stores without the latency and overhead typically associated with batch processing systems like Hive.

Since its inception, Presto has evolved into two major projects:

• PrestoDB, maintained by the Presto Software Foundation and used by companies like Facebook and Uber.

• Trino (formerly known as PrestoSQL), a fork maintained by the original Presto creators, focusing on broader community contributions and feature evolution.

At its core, Presto is a read-only, MPP (massively parallel processing) engine that supports ANSI SQL and allows users to run federated queries across diverse data sources such as:

• Amazon S3

• Apache Hive

• MySQL

• PostgreSQL

• Cassandra

• Kafka, and more

Presto is particularly effective in environments where organizations want to query data in place—across data lakes and databases—without moving or duplicating the data.

It’s widely adopted in big data infrastructures and often used in combination with tools like Hive Metastore and Apache Ranger for metadata and security.

Because Presto does not store data itself, it relies entirely on external storage systems, making it a flexible, decoupled query layer suitable for hybrid cloud and multi-source analytics.

🔍 Looking for a comparison between other high-performance engines? See Clickhouse vs Druid to understand how Presto stacks up in certain workloads.

🧠 You can also explore how Presto fits into broader data pipeline automation efforts in our post on Automating Data Pipelines with Apache Airflow.

What is Dremio?

Dremio is an open-source, self-service data platform designed to accelerate SQL queries directly on cloud data lakes.

Launched to simplify and modernize analytics workflows, Dremio aims to make querying large-scale datasets more efficient without the need for complex ETL pipelines or traditional data warehouses.

Unlike Presto, which is purely a query engine, Dremio provides a more comprehensive analytics platform.

It comes with an intuitive UI, a semantic layer for business-friendly data modeling, and a built-in data catalog that makes it easier for analysts to discover, curate, and manage datasets.

Some of Dremio’s key features include:

• Reflections: Dremio’s proprietary query acceleration technology that precomputes and stores optimized representations of data, drastically improving performance for repetitive queries.

• Apache Arrow and Iceberg support: Dremio is natively built on Apache Arrow, allowing for high-speed in-memory processing, and has tight integration with Apache Iceberg—a modern table format for big data analytics.

• Semantic Layer: Provides a unified, governed view of data, enabling consistent metrics and easy data exploration.

• Built-in UI: A visual, self-service interface that allows analysts and data scientists to run and share queries without writing complex code.

• Data lakehouse architecture: Dremio blends the flexibility of data lakes with the structure and performance of data warehouses, positioning itself as a lakehouse platform.

Dremio supports modern data formats like Parquet, Iceberg, and Delta Lake, and is designed for direct querying on object stores such as Amazon S3, Azure Data Lake Storage, and Google Cloud Storage—eliminating the need to move data into external systems.

💡 Curious about how other data engines are evolving around lakehouse architectures? Check out Druid vs Kudu and Presto vs Druid.

📘 Learn more about Apache Iceberg from the official Iceberg documentation.

By combining a performance-optimized execution engine with intelligent caching and a robust user experience, Dremio is especially appealing to teams looking to democratize data access without compromising on performance or governance.

Presto vs Dremio: Architecture Comparison

When evaluating Presto vs Dremio, understanding the underlying architecture of each system is crucial.

Both engines are built for scalable, distributed SQL querying, but their designs reflect very different priorities and approaches to analytics on data lakes.

Presto Architecture

Presto follows a decoupled, federated query architecture.

It’s a compute-only engine that does not manage or store data.

Instead, it connects to various data sources—like Hive, S3, or MySQL—via connectors and performs in-memory, distributed processing across worker nodes.

Key architectural components:

• Coordinator: Parses SQL queries, plans execution, and assigns tasks to workers.

• Workers: Execute tasks in parallel; stateless and do not store intermediate results.

• Connectors: Interface to connect to data sources like Hive Metastore, S3, and relational databases.

• No storage layer: Purely reads from external sources.

Dremio Architecture

Dremio is a lakehouse engine with an integrated architecture designed to accelerate analytics workloads natively on data lakes.

Unlike Presto, Dremio includes a storage layer, semantic layer, and a UI/SQL editor, making it more of an end-to-end platform for data teams.

Key architectural components:

• Query Execution Engine: Built on Apache Arrow for high-performance columnar processing.

• Reflections: Precomputed data structures stored for query acceleration.

• Semantic Layer: Provides logical modeling, business definitions, and user-level abstractions.

• Catalog & Metadata Layer: Integrated data catalog for discoverability and governance.

• UI + API Access: Analysts and engineers can use both interfaces for interaction.

Presto vs Dremio Architecture: Side-by-Side

In short, Presto offers flexibility and speed for federated queries across diverse systems, while Dremio is engineered for high-performance, governed analytics on modern data lakes.

Presto vs Dremio: Performance and Optimization

When comparing Presto vs Dremio, performance is a crucial deciding factor—especially for teams running frequent queries, interactive dashboards, or ad hoc analytics.

Both engines excel in distributed SQL processing, but they differ in how they optimize queries and handle workloads at scale.

Presto Performance

Presto is known for its high-performance query execution across large-scale, distributed datasets.

It’s especially effective for:

• Batch analytics

• Federated queries across multiple heterogeneous sources (e.g., Hive + MySQL + Kafka)

However, Presto lacks a native caching or materialization layer, meaning it executes every query from scratch.

This can be a limitation for repetitive dashboard queries or time-sensitive workloads where caching or precomputation would improve response times.

Additionally, Presto’s performance can degrade if:

• Underlying data sources are slow

• Queries span multiple systems

• It’s used for highly concurrent, low-latency workloads (e.g., powering BI dashboards)

📊 For systems better optimized for repeated visualization workloads, see Grafana vs Tableau or Superset vs Power BI.

Dremio Performance

Dremio was built with performance optimization at its core, especially for interactive workloads and BI use cases.

Its most notable optimization feature is:

• Reflections: Dremio’s proprietary acceleration layer that creates materialized views in the background. These are transparently used to accelerate queries without changing the SQL.

Other performance advantages include:

• Native support for Apache Arrow: Dremio processes data in Arrow’s columnar in-memory format, which drastically reduces serialization overhead and improves CPU efficiency.

• Column pruning and pushdowns: It intelligently rewrites queries to read only the necessary columns and rows.

• Iceberg integration: For fine-grained partitioning and snapshot isolation.

Dremio excels when used to:

• Power BI dashboards with frequent and repetitive queries

• Support interactive analysis on large cloud data lakes

• Optimize cost and performance by querying data in-place, without ingestion

🧠 Learn more about Dremio’s performance principles from the official Dremio query acceleration guide.

In short, Presto shines in flexibility and federated query scenarios, while Dremio delivers superior performance for repeat queries and dashboard workloads, thanks to its query acceleration layer and in-memory execution model.

Presto vs Dremio: Use Cases and Ideal Scenarios

Both Presto and Dremio are powerful engines for querying large datasets, but their strengths align with different organizational needs and technical workflows.

Choosing the right tool depends on your data architecture, team composition, and analytics requirements.

When to Use Presto

Presto is ideal when flexibility, scalability, and federated query capabilities are top priorities.

It’s often favored by engineering teams with deep infrastructure control and a wide variety of data sources.

Best suited for:

• Federated analytics: Querying across multiple data sources like Hive, MySQL, and Kafka in a single SQL statement.

• Ad-hoc exploration: Analysts or data scientists exploring large-scale data without needing interactive dashboards.

• Big data environments: Where query engines are used as compute layers in modern data platforms.

Organizations that use tools like Apache Hive, Iceberg, or Presto in Kubernetes-based deployments (see Terraform Kubernetes Deployment) often find Presto a natural fit.

✅ Want to scale Presto-based deployments? Read our guide on Kubernetes Scale Deployment to learn how to dynamically scale compute resources.

When to Use Dremio

Dremio is purpose-built for interactive, low-latency analytics on data lakes, with a focus on self-service usability and performance.

Its UI, semantic layer, and acceleration features make it especially effective for data teams supporting business intelligence users.

Best suited for:

• Interactive dashboards: Optimized for tools like Tableau, Power BI, and Superset.

• Self-service data access: Business analysts can explore and query data without relying heavily on engineering.

• Governed lakehouse environments: Dremio offers a semantic layer and fine-grained data governance for modern lakehouses.

If your team is already evaluating BI platforms, you might also want to compare Superset vs Power BI or explore backend tools like Metabase vs Kibana for context on self-service analytics.

In summary, choose Presto if you’re operating a federated data environment with engineering-driven workflows.

Choose Dremio if you’re building a user-friendly, high-performance lakehouse with interactive BI at scale.

Presto vs Dremio: Integration with BI and Tools

A major factor in choosing between Presto vs Dremio is how well each platform integrates with business intelligence tools like Tableau, Superset, or Power BI.

While both engines support SQL-based analytics, they differ significantly in ease of integration, data modeling, and user accessibility.

Presto: Flexible but Developer-Driven

Presto provides broad compatibility with most BI tools through standard ODBC/JDBC drivers.

It can be connected to:

• Tableau

• Superset

• Power BI

• Looker

However, Presto requires external configuration for things like:

• Data modeling

• Governance and access controls

• Semantic layers or user-friendly datasets

This often puts more responsibility on data engineers to curate access and create abstractions for business users.

Dremio: Built for BI and Self-Service

Dremio takes a more opinionated, integrated approach to BI integration. It offers:

• Native connectors to Tableau, Power BI, and Excel

• A semantic layer for business-friendly naming and governance

• A visual query builder that empowers non-technical users to explore data without writing SQL

These built-in features reduce the burden on engineers and make Dremio ideal for self-service analytics environments where business users need direct access to clean, performant data.

Dremio also supports Apache Arrow Flight, a high-speed interface for transferring query results to BI tools with lower latency than JDBC/ODBC.

💡 Want to explore platforms with integrated query builders and semantic layers? You might also be interested in Metabase vs Kibana or Superset vs Metabase.

In short, Presto integrates well with BI tools but demands more engineering overhead, while Dremio offers a smoother out-of-the-box experience with governance, modeling, and interactivity built into the platform.

Presto vs Dremio: SQL Features and Developer Experience

Beyond performance and integration, the developer experience—especially how teams write queries, browse schemas, and explore data—is a key consideration in the Presto vs Dremio comparison.

Both platforms are highly SQL-centric, but they diverge in tooling and user interfaces available out of the box.

Presto Developer Experience

Presto supports ANSI-compliant SQL and is well-suited for developers comfortable with command-line tools or integrating into larger pipelines. However:

• It lacks a native UI

• Data exploration typically requires connecting to a BI or third-party interface

• Metadata and schema discovery depend on integration with external catalogs like Hive Metastore or AWS Glue

This means teams using Presto usually set up external tooling for query execution, metadata browsing, and collaboration.

💡 If you’re already running Apache Airflow or Superset, you may want to check out Automating Data Pipelines with Apache Airflow or Airflow Deployment on Kubernetes for end-to-end orchestration ideas.

Dremio Developer Experience

Dremio enhances the developer and analyst experience with a feature-rich web interface, offering:

• A visual SQL editor with autocomplete

• Notebooks for collaborative query development

• An integrated data catalog and semantic layer

• SQL pushdown capabilities for optimizing execution at the storage layer (e.g., with Iceberg)

For teams prioritizing usability, governance, and data discoverability, Dremio provides a more turnkey solution with minimal setup.

In summary, Presto is better for teams who already have external tooling in place and want full flexibility. Dremio is ideal for teams that want a unified, visual platform for data discovery, authoring, and governance without relying on third-party services.