Presto Pricing & Plans

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Distributed Query Engine

Executes SQL queries in parallel across clusters of nodes for fast and scalable analytics.

ANSI SQL Support

Provides compliance with standard SQL including joins, subqueries, and aggregations.

In-Memory Execution

Uses memory-based query processing to minimize disk I/O and improve performance.

MPP Architecture

Massively parallel processing with coordinator and worker nodes for scalable execution.

Connector Framework

Supports diverse data sources like HDFS, S3, MySQL, Cassandra, and Elasticsearch.

Horizontal Scalability

Easily scales by adding worker nodes to handle growing datasets and workloads.

Ad-Hoc Analysis

Optimized for interactive and exploratory queries to enable real-time data insights.

Low Query Latency

Delivers fast response times using pipelined execution and memory-optimized design.

Data Source Transparency

Queries data directly in place without requiring data movement or ingestion.

Resource Management

Integrates with resource managers to optimize cluster utilization and efficiency.

Cost-Based Optimization

Applies join reordering, predicate pushdown, and statistics-driven planning.

BI Tool Integration

Connects to visualization tools like Tableau and Power BI for seamless analytics.

OLAP Optimization

Tailored for analytical workloads and reporting rather than transactional processing.

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Brand Name	Presto Foundation
Information	Supporting and sustaining the Presto community
Founded Year	2019
Director/Founders	Dain Sundstrom, David Phillips, Martin Traverso, Michael Cheng, Nezih Yigitbasi
Company Size	101-500 Employees