Dremio vs Presto: What are the differences?

Introduction

In this article, we will explore the key differences between Dremio and Presto, two popular data query engines. Both Dremio and Presto are used for querying and analyzing large volumes of data in a distributed fashion, but they have some distinct features and functionalities that set them apart.

1. Data Source Support: Dremio supports a wide range of data sources, including relational databases (like MySQL, PostgreSQL), NoSQL databases (like MongoDB), cloud storage services (like Amazon S3) and even Hadoop distributed file systems. On the other hand, Presto has limited data source support and primarily focuses on querying data stored in Hadoop, cloud storage, and relational databases.

2. Architecture: Dremio has a self-contained architecture, where it includes its own distributed file system called DremioFS and provides an end-to-end solution for data querying. In contrast, Presto follows a more modular architecture, where it separates query execution engine (Presto Coordinator) from data storage (Presto Worker), allowing flexibility in deploying them on different resources.

3. Network Optimization: Dremio utilizes a combination of data locality awareness, columnar layout, and intelligent caching to optimize data access through the network. This enables Dremio to efficiently fetch only the required data from remote nodes and minimize network traffic. Presto, on the other hand, relies on a more traditional query planner and execution model, which may result in higher network overhead for data retrieval.

4. SQL Compatibility: Both Dremio and Presto support SQL and offer a similar set of SQL functionalities. However, Dremio provides enhanced SQL capabilities, including support for complex data types, nested queries, window functions, and other advanced SQL features. Presto, while powerful in SQL processing, may have some limitations in terms of complex data types and advanced queries.

5. Administration and Management: Dremio offers a comprehensive web-based user interface for managing and monitoring the system, including fine-grained access control, query profiling, and performance tuning capabilities. Presto, on the other hand, primarily relies on command-line tools and configuration files for system administration. While Presto provides some level of monitoring and management features, it may require additional external tools for more advanced administration tasks.

6. Deployment Flexibility: Dremio provides a single, integrated platform that can be deployed in an on-premises environment or in the cloud. It also offers a Kubernetes-based deployment option for containerized environments. Presto, on the other hand, is designed to work in a distributed ecosystem and can be deployed across a cluster of machines or integrated with other big data processing frameworks like Hadoop or Apache Hive.

In summary, Dremio and Presto have some significant differences in terms of data source support, architecture, network optimization, SQL compatibility, administration, and deployment flexibility. However, both offer powerful data querying capabilities and are suited for different use cases depending on specific requirements.