 |
Apache Impala vs Presto
May 26, 2023 | Author: Michael Stromann
8
Apache Impala is a modern, open source, distributed SQL query engine for Apache Hadoop.
10
Presto is a highly parallel and distributed query engine for big data, that is built from the ground up for efficient, low latency analytics.
Apache Impala and Presto are both open-source distributed SQL query engines designed for interactive analytics on big data. They share similarities in their goals of providing fast and interactive querying capabilities, but there are differences in their architectures and ecosystems.
Apache Impala, developed by Cloudera, is tightly integrated with the Hadoop ecosystem and specifically designed to deliver high-performance SQL queries on data stored in Apache Hadoop Distributed File System (HDFS) and Apache HBase. Impala uses a massively parallel processing (MPP) architecture, where data is stored in a columnar format and computation is distributed across multiple nodes for parallel execution. It offers low-latency queries and is known for its efficient handling of complex queries.
Presto, on the other hand, is an open-source distributed SQL query engine developed by Facebook. It is designed to run on various data sources, including Hadoop, traditional relational databases, and cloud storage systems. Presto follows a distributed architecture and operates in a federated manner, where data is accessed from different sources and processed in-memory across a cluster of machines. Presto focuses on query flexibility and supports ANSI SQL standards, making it compatible with various data sources and allowing users to query multiple systems with a single SQL statement.
See also: Top 10 Big Data platforms
Apache Impala, developed by Cloudera, is tightly integrated with the Hadoop ecosystem and specifically designed to deliver high-performance SQL queries on data stored in Apache Hadoop Distributed File System (HDFS) and Apache HBase. Impala uses a massively parallel processing (MPP) architecture, where data is stored in a columnar format and computation is distributed across multiple nodes for parallel execution. It offers low-latency queries and is known for its efficient handling of complex queries.
Presto, on the other hand, is an open-source distributed SQL query engine developed by Facebook. It is designed to run on various data sources, including Hadoop, traditional relational databases, and cloud storage systems. Presto follows a distributed architecture and operates in a federated manner, where data is accessed from different sources and processed in-memory across a cluster of machines. Presto focuses on query flexibility and supports ANSI SQL standards, making it compatible with various data sources and allowing users to query multiple systems with a single SQL statement.
See also: Top 10 Big Data platforms
Apache Impala vs Presto in our news:
2019. Starburst raises $22M to modernize data analytics with Presto
Starburst, the company seeking to commercialize the open-source Presto distributed query engine for big data (originally developed at Facebook), has announced a successful funding round, raising $22 million. The primary objective of Presto is to enable anyone to utilize the standard SQL query language for executing interactive queries on vast amounts of data stored across diverse sources. Starburst intends to monetize Presto by introducing several enterprise-oriented features. These additions will primarily focus on enhancing security, such as role-based access control, and integrating connectors to enterprise systems like Teradata, Snowflake, and DB2. Additionally, Starburst plans to provide a management console that empowers users to configure the cluster for automatic scaling, among other functionalities.
