Data Analytics

What Are Athena, Apache Spark, AWS Glue, Trino, and Presto? A Simple Guide

Modern companies collect huge amounts of data from applications, websites, logs, payments, sensors, CRM systems, and internal tools. Storing this data is only the first step. To get value from it, companies need tools that can query, transform, clean, and analyze it.

Amazon Athena, Apache Spark, AWS Glue, Trino, and Presto are common tools used in modern data platforms. They are often mentioned together, but they solve different problems.

They are all foundational big data technologies used to store, clean, process, and query massive datasets at scale. While some are managed cloud services from Amazon Web Services (AWS), others are open-source frameworks that actually power those cloud services behind the scenes.

Why Companies Need These Tools

Many companies store raw data in Amazon S3 as CSV, JSON, logs, or Parquet files. Business and engineering teams then need to answer questions such as:

• How many customers signed up yesterday?
• Which products generate the most revenue?
• Why did application errors increase?
• Which marketing channel performs best?
• How much storage and query cost is the company paying?

Without analytics tools, engineers would need to download files, write scripts, process data manually, and build custom reporting systems. That becomes slow, expensive, and difficult to maintain.

Amazon Athena

Amazon Athena is a serverless SQL query service from AWS. It lets users run SQL queries directly on data stored in Amazon S3.

With Athena, companies do not need to manage database servers. They store files in S3 and query them using SQL.

• What it is: A serverless, interactive analytics service provided by AWS.
• Core function: It allows you to write standard SQL queries directly against files stored in Amazon S3 (like CSV, JSON, Parquet, or ORC) without moving the data.
• How it works: It is completely serverless—you do not manage any servers or infrastructure. You simply point Athena to your data, define a schema, and start querying.
• Pricing: You pay only for the queries you run, typically based on the amount of data scanned.

Companies use Athena for

• Ad-hoc SQL analysis
• Business reports
• Dashboards
• Log analysis
• Security investigations
• Cost analysis

Athena works especially well with Apache Parquet because Parquet allows Athena to read only the columns needed for a query.

AWS Glue

AWS Glue is a managed data integration service from AWS. It helps companies prepare, transform, and catalog data without managing infrastructure.

Glue is often used to run ETL jobs and maintain metadata about datasets. This metadata can then be used by Athena, Spark, and other analytics tools.

• What it is: A serverless data integration and automated Extract, Transform, and Load (ETL) service.
• Core function: Core function: Glue helps you discover, clean, and prepare data for analysis. It has two primary parts:
  • Glue Data Catalog: A searchable index that acts as a central registry to store metadata (table definitions and schemas) for your data lakes.
  • Glue ETL Jobs: A fully managed infrastructure that automatically generates and executes Apache Spark scripts to move and transform data.
• Relationship: Amazon Athena relies heavily on the AWS Glue Data Catalog to know the schema and layout of the files it is querying in S3.

Companies use Glue for

• Scheduled ETL jobs
• Data catalog management
• Schema discovery
• Data lake preparation
• Moving data between systems

Apache Spark

Apache Spark is a distributed data processing engine. It is designed to process very large datasets across many machines.

Spark is not only for querying data. It is commonly used to clean, transform, join, aggregate, and prepare data before it is used by other systems.

• What it is: An open-source, lightning-fast distributed general processing engine for big data.
• Core function: Spark excels at heavy-duty data transformation (ETL), machine learning, and stream processing across massive computer clusters.
• How it works: It processes data in-memory, making it much faster than older legacy systems like Hadoop MapReduce. Developers write Spark code using Python (PySpark), Scala, Java, or SQL.
• Relationship: AWS uses Spark under the hood for several managed tools (like AWS Glue and Amazon Athena for Apache Spark).

Companies use Spark for

• ETL pipelines
• Large-scale data transformation
• Machine learning data preparation
• Batch processing
• Feature engineering
• Converting raw data into optimized formats

Presto

Presto is a distributed SQL query engine originally developed at Facebook. It was created to run fast analytical queries on large datasets.

Trino came from the Presto project. Today, both tools still exist. Many companies continue using Presto, while many new deployments choose Trino.

• What it is: An open-source, distributed SQL query engine originally created by Facebook (Meta) in 2012.
• Core function: Presto was designed from day one to run fast, interactive analytical queries against data of any size—ranging from gigabytes to petabytes.
• How it works: Unlike traditional databases, Presto does not have its own storage engine. Instead, it connects directly to external storage systems (like Hive, S3, Cassandra, or relational databases) to run queries in place.
• Relationship: Amazon Athena was originally built using a fork of Presto as its primary SQL execution architecture.

Companies use Presto for

• Distributed SQL analytics
• Data lake queries
• Interactive reporting
• Large-scale business intelligence

Trino

Trino is a distributed SQL query engine. It allows companies to run fast SQL queries across large datasets and multiple data sources.

One of Trino's main strengths is federated querying. This means one query can read data from different systems, such as S3, PostgreSQL, MySQL, Hive, Iceberg, and other sources.

• What it is: An open-source distributed SQL query engine that is the direct evolutionary successor to Presto.
• Core function: Trino does the exact same job as Presto: fast, federated SQL queries over massive, multi-source data lakes.
• The Origin Story: In 2019, the original creators of Presto left Facebook due to governance disagreements. They split the project into a new branch called PrestoSQL, which was officially rebranded as Trino in 2020. The Facebook-controlled version remains PrestoDB. Trino has since gained massive industry adoption and faster feature updates.
• Relationship: AWS updated Amazon Athena (Engine Version 3) to run on Trino, making Trino the primary backend compute layer for Athena's standard SQL engine.

Companies use Trino for

• Interactive analytics
• Large SQL workloads
• Querying data lakes
• Combining data from multiple systems
• BI and reporting platforms

How These Tools Work Together

These tools are not always direct competitors. In many companies, they are used together as part of the same data platform.

Applications

CSV / JSON / Logs
    ↓
Amazon S3
    ↓
AWS Glue / Apache Spark / Parqify (CSV/JSON → Optimized Parquet)
    ↓
Amazon S3
Optimized Apache Parquet
    ↓
Athena / Spark / Glue / Trino / Presto
    ↓
Dashboards, reports, and analytics

Why Apache Parquet Matters

All of these tools work very well with Apache Parquet. Parquet is a columnar file format designed for analytics.

Compared to CSV or JSON, Parquet can reduce file size, improve query speed, and lower the amount of data scanned by analytics engines.

Parquet helps because it supports

• Columnar storage
• Efficient compression
• Schema information
• Column pruning
• Predicate pushdown
• Faster analytics queries

Where Parqify Fits

Parqify helps prepare data before it is queried by Athena, Spark, Glue, Trino, or Presto.

It converts CSV and JSON files from Amazon S3 into optimized Apache Parquet. Instead of building custom scripts or complex ETL pipelines, teams can use Parqify to create analytics-ready Parquet files faster.

Parqify also provides optimization profiles for different engines, helping teams choose Parquet settings that fit their analytics platform.