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Easier integration with ecosystems

Presto is designed to integrate seamlessly with the existing data ecosystem without any modification to the ongoing system. It's like boosting your existing stack with an extra faster data provider.

Presto provides an additional layer of computing to speed up analysis. It does not store data, which gives it the great advantage of being able to query resources up and down according to demand.

This separation of computing and storage makes the Presto query engine ideal for cloud environments. Most cloud deployments utilize object storage, which has been separated from the computing layer and automatically scaled to optimize resource costs.

Unified SQL interface

SQL is by far the oldest and most widely used data analysis language. Analysts, data engineers, and data scientists use SQL to explore data, build dashboards, and test hypotheses with notebooks such as Jupyter and Zeppelin or BI tools such as Tableau, PowerBI, and Looker.

Presto is a federated query engine that can query data not only from distributed file systems, but also from other sources, such as NoSQL stores such as Cassandra, Elasticsearch, and RDBMS, and even message queues such as Kafka.

Performance

The Facebook team developed Presto because Apache Hive is not suitable for interactive queries. Hive's underline architecture executes queries by executing multiple MapReduce and Tez jobs, which is very suitable for large and complex jobs, but not for low-latency queries. The Hive project recently introduced in-memory caching using Hive LLAP; however, it is suitable for some types of queries, but it also makes Hive more resource-intensive.

Similarly, Apache Spark is ideal for large and complex jobs that use memory computing. However, it is not as effective as Presto interactive BI queries.

Presto is built for high performance with a number of key features and optimizations, such as code generation, in-memory processing, and pipelined execution. Presto queries share a long-standing Java virtual machine (JVM) process on the worker node, thus avoiding the overhead of generating new JVM containers.

Query federation

Presto provides a unified SQL dialect that abstracts all supported data sources. This is a powerful feature, and users do not need to know the connection of the underlying system and the SQL dialect.

A design for the cloud

Presto's basic design of separating storage and computing makes it extremely easy to operate in a cloud environment. Because the Presto cluster does not store any data, it can scale automatically according to the load without causing any data loss.

As you can see, Presto provides many advantages for interactive ad hoc queries. No wonder data platform teams are increasingly using Presto as the de facto SQL query engine to run analysis across data sources without the need to move data.

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