Spark SQL vs Presto

Spark SQL and Presto, have become increasingly popular due to their capabilities in processing large amounts of data from various sources. In this blog post, we will dive deeper into Spark SQL and Presto, discussing their similarities, differences, and how they can be utilized to meet your specific data processing needs.

How these two tools are similar:

  • Both of these software frameworks are open source and are designed to handle large amounts of data. They operate in a distributed, parallel, and in-memory manner, which enables them to process data at high speeds.
  • BI tools connect with these frameworks using JDBC/ODBC connections.
  • They have been thoroughly tested and deployed by companies that process petabytes of data.
  • These frameworks can be executed either on-premises or in the cloud, and they can be containerized for a flexible and scalable deployment option.


  • Presto is a query engine that provides access to and consolidation of data from various data sources using ANSI SQL:2003. It is generally deployed as a middle-layer for federation.On the other hand, Spark is a versatile cluster-computing framework that does not natively support SQL. To add structured data processing capabilities to Spark, you must install the Spark SQL module, which is also ANSI SQL:2003 compliant since Spark 2.0.
  • Presto is frequently used to support interactive SQL queries that are mainly analytical in nature but can also execute SQL-based ETL operations. Spark has more general-purpose applications, often utilized for data transformation and machine learning workloads.
  • By default, Presto allows querying of data in object stores like S3 and has many connectors available. It also works exceptionally well with Parquet and Orc format data.In contrast, Spark must rely on Hadoop file APIs to access S3 or purchase Databricks features. It also has limited connectors for data sources.

Many users are today are learning about Presto Spark. This lays out many of the differences on Presto vs Spark SQL and how Spark and Presto can be compared.

If you want to deploy a Presto cluster on your own, we recommend checking out how Ahana manages Presto in the cloud. We put together this free tutorial that shows you how to create a Presto cluster.

You can see our previous guide to compare the Spark execution engine vs Presto, or our comparison between Databricks and AWS Athena.

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