There are several popular ways to query data directly from Amazon S3. Here are some of the most commonly used methods:

1. Amazon S3 Select: S3 Select is a feature that allows you to run SQL queries on data stored in S3 objects. This can be a good option if you need to query small amounts of data or if you need to filter the results of a query.

2. Amazon Athena: Amazon Athena is a serverless query service that allows you to run SQL queries directly on data stored in Amazon S3. It supports various data formats and provides an interactive query experience with minimal setup and management.

3. Amazon Redshift Spectrum: Amazon Redshift Spectrum is an extension of Amazon Redshift, a data warehousing service. It enables you to run complex queries that join data stored in S3 with data in your Redshift cluster. This service is suitable for analytical workloads.

4. AWS Glue: AWS Glue is a fully managed extract, transform, and load (ETL) service that also provides a way to define and execute SQL-like queries on data stored in S3. It can be used to prepare and transform data before querying.

5. Presto: Presto is an open-source distributed SQL query engine that can be configured to query data in Amazon S3. It's highly customizable and can handle large-scale, interactive queries across various data sources.

6. Apache Hive: Hive is another open-source data warehousing and SQL-like query language that can be used to query data in S3. It provides a familiar SQL interface for querying and managing large datasets.

7. Spark SQL: If you're using Apache Spark for data processing, Spark SQL allows you to execute SQL queries on your Spark DataFrame, which can include data stored in Amazon S3. This is useful for combining data processing and querying.

8. PrestoDB: Similar to Presto, PrestoDB is an open-source distributed SQL query engine that can be deployed on your infrastructure. It's designed for high-speed querying of large datasets, including those in S3.

9. EMR (Elastic MapReduce): Amazon EMR is a managed Hadoop and Spark service that allows you to process and query large datasets. You can configure EMR to read data from S3 and use Hive or Spark SQL for querying.

10. Custom Applications: You can develop custom applications using SDKs like the AWS SDK for Python (Boto3) or AWS SDK for Java. These applications can directly access and process data from S3 using APIs, enabling you to build tailored querying solutions.

Each of these methods has its own advantages and use cases. Your choice will depend on factors such as the complexity of your queries, the size of your datasets, the desired level of management, and the tools or platforms you're already using for data processing and analytics.