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What is Trino?

Trino is an exceptionally swift query engine engineered for remarkable performance. This high-efficiency, distributed SQL query engine is specifically designed for big data analytics, allowing users to explore their extensive data landscapes. Built for peak efficiency, Trino shines in low-latency analytics and is widely adopted by some of the biggest companies worldwide to execute queries on exabyte-scale data lakes and massive data warehouses. It supports various use cases, such as interactive ad-hoc analytics, long-running batch queries that can extend for hours, and high-throughput applications that demand quick sub-second query responses. Complying with ANSI SQL standards, Trino is compatible with well-known business intelligence tools like R, Tableau, Power BI, and Superset. Additionally, it enables users to query data directly from diverse sources, including Hadoop, S3, Cassandra, and MySQL, thereby removing the burdensome, slow, and error-prone processes related to data copying. This feature allows users to efficiently access and analyze data from different systems within a single query. Consequently, Trino's flexibility and power position it as an invaluable tool in the current data-driven era, driving innovation and efficiency across industries.

What is DuckDB?

Managing and storing tabular data, like that in CSV or Parquet formats, is crucial for effective data management practices. It's often necessary to transfer large sets of results to clients, particularly in expansive client-server architectures tailored for centralized enterprise data warehousing solutions. The task of writing to a single database while accommodating multiple concurrent processes also introduces various challenges that need to be addressed. DuckDB functions as a relational database management system (RDBMS), designed specifically to manage data structured in relational formats. In this setup, a relation is understood as a table, which is defined by a named collection of rows. Each row within a table is organized with a consistent set of named columns, where each column is assigned a particular data type to ensure uniformity. Moreover, tables are systematically categorized within schemas, and an entire database consists of a series of these schemas, allowing for structured interaction with the stored data. This organized framework not only bolsters the integrity of the data but also streamlines the process of querying and reporting across various datasets, ultimately improving data accessibility for users and applications alike.

Media

Media

Integrations Supported

Databricks
DbVisualizer
Dosi
Kestra
SQL
Unity Catalog
Amazon S3
Apache Iceberg
Apache Phoenix
DQOps
Embeddable
GrowthBook
Onehouse
SecuPi
Simba
StarRocks
WrenAI

Integrations Supported

Databricks
DbVisualizer
Dosi
Kestra
SQL
Unity Catalog
ElectroNeek
Flyte
Vanna.AI

API Availability

API Availability

Has API

Pricing Information

Free
Open source
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Windows

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Trino

Company Website

trino.io

Company Facts

Organization Name

DuckDB

Company Website

duckdb.org

Categories and Features

Big Data

Not specified

Database

Not specified

OLAP Databases

Not specified

Query Engines

Not specified

Categories and Features

Database

Not specified

Embedded Database

Not specified

OLAP Databases

Not specified

Query Engines

Not specified

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