Druid vs Presto: What are the differences?

Introduction:

In this article, we will compare and analyze the key differences between Druid and Presto, two widely used data processing technologies.

1. Scalability and Workload Management: Druid is designed for scalable, real-time analytics on large datasets. It excels at serving low-latency queries by pre-calculating and indexing data in a specialized data store. On the other hand, Presto is a distributed SQL query engine that focuses on providing fast ad hoc queries on various data sources, but it does not provide built-in support for real-time analytics or indexing.

2. Data Storage and Query Capabilities: Druid stores data in column-oriented, compressed formats, enabling fast aggregations and filtering. It offers a wide range of advanced analytic features such as roll-ups, approximations, and time-series analysis. Presto, on the other hand, can query various data sources directly using standard SQL syntax, allowing it to access a broader range of data formats and systems, including relational databases and files.

3. Processing Paradigm: Druid is a data processing system specifically optimized for time series data and event-driven analytics. It is built on the concept of data ingestion, indexing, and query execution. Presto, on the other hand, is a general-purpose distributed SQL query engine that can process data across multiple distributed nodes in a parallel and scalable manner. It can handle a wide range of query workloads, from simple ad hoc queries to highly complex analytical queries.

4. Data Latency: Druid focuses on providing low-latency queries for real-time analytics. It achieves this by leveraging pre-calculated aggregations and an indexed data store. This makes it ideal for interactive, real-time dashboards and applications. Presto, while it can handle queries in near real-time, may have slightly higher latencies compared to Druid, especially when dealing with large datasets or complex analytical queries.

5. Ecosystem and Integration: Druid has a strong ecosystem with built-in support for Apache Hadoop, Apache Kafka, and other big data technologies. It integrates well with popular BI tools and frameworks such as Apache Superset and Tableau. Presto, on the other hand, offers extensive integration capabilities with various data sources and systems, including Hadoop, Cassandra, MySQL, and more. It supports JDBC and ODBC connectors, making it compatible with a wide range of BI and visualization tools.

6. Security and Authentication: Druid provides robust security features, including encryption at rest and in transit, role-based access control, and auditing capabilities. It supports integration with external authentication providers like LDAP and Kerberos. Presto also offers security features such as SSL encryption and access control, but it lacks some advanced security functionalities found in Druid, such as data encryption at rest.

In summary, Druid is designed for scalable, real-time analytics with low-latency queries, specialized indexing, and advanced analytic features. Presto, on the other hand, is a distributed SQL query engine that excels at ad hoc querying across various data sources, providing extensive compatibility and integration capabilities.