Introduction to Presto: Open Source SQL Query Engine that's changing Big Data Analytics

In today's data-driven world, organizations face a constant challenge: how to analyse massive datasets quickly and efficiently without moving data between disparate systems. Presto, an open-source distributed SQL query engine that's revolutionizing how we approach big data analytics.

What is Presto?

Presto is an open-source distributed SQL query engine designed for fast interactive analysis of data at any scale. Unlike traditional database systems that require data to be loaded into their proprietary storage format, Presto can query data directly where it lives – be it Hadoop, AWS S3, Google Cloud Storage, Relational Databases, NoSQL systems, or even custom data sources.

Presto Architecture allows you:

1. Query data across multiple sources without ETL (Extract, Transform & Load).
2. Process petabytes of data with sub-second query response times.
3. Use familiar ANSI SQL syntax for complex analytics.
4. Scale resources independently of your data volume.

The Origin Story: From Facebook to Global Adoption

Presto was born in 2012 at Facebook (now Meta) when engineers faced a challenge: Facebook's data analysts were waiting hours for their Hive queries to complete, severely limiting their productivity.

The team set out to build a new query engine that could provide interactive query speeds on Facebook's massive 300PB data warehouse. Within a few months, they had a prototype that was 10x faster than Hive for many workloads, and by 2013, Facebook open-sourced Presto to the world.

Since then, Presto has been adopted by technology giants like Uber, Netflix, Twitter, and Airbnb, as well as countless enterprises across industries.

Coordinator Node (👨💼)

The coordinator is the brain of the operation:

• Accepts SQL queries from clients
• Parses and analyzes queries
• Creates and optimizes query execution plans
• Distributes work to worker nodes
• Tracks progress and returns results to clients

Worker Nodes (👷,👷,👷)

Workers are the computational workhorses:

• Execute tasks assigned by the coordinator
• Read data from source systems through connectors
• Process data in parallel
• Exchange intermediate results with other workers
• Return final results to the coordinator

Connectors (🔌)

Connectors are Presto's interfaces to data sources:

• Abstract away the details of different storage systems
• Handle data location, format, and access methods
• Translate between Presto's internal representation and source data formats
• Enable seamless querying across disparate systems

Real-World Use Cases

1. Data Lake Analytics

2. Federated Queries Across Systems

3. Interactive BI & Dash-boarding (Real Time Analytics)

Note: Presto is not a Database (⛔) and it doesn't replace databases.        

In the next article, we will see how to install Presto locally in the system and to query data from different data sources.

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