TRINO Series

• PRESTO / Trino Basics
• Trino Series: Building a Sample Project on Local Installation
• Trino Series: Optimizing and Expanding the Sample Project
• Trino Series: Advanced Integrations with Cloud Storage
• Trino Series: Caching Strategies and Query Performance Tuning

Introduction: Scaling Data with Cloud Storage

In the previous blogs, we explored building a sample project locally, optimizing queries, and adding real-time data streaming. Now, let’s take our Trino project a step further by connecting it to cloud storage, specifically Amazon S3. This integration will showcase how Trino can handle large datasets beyond local storage, making it suitable for scalable, cloud-based data warehousing. By connecting Trino to S3, we can expand our data analytics project to manage vast datasets with flexibility and efficiency.

Project Enhancement Overview

Goals for This Blog Post

1. Integrate Amazon S3 with Trino: Configure Trino to access data stored in S3 buckets, setting up Trino to function as a data lake query engine.
2. Query Data Directly from S3: Execute SQL queries on large datasets in S3 without moving data locally.
3. Use Cases for Cloud Data Warehousing: Explore how cloud storage integration supports diverse analytics use cases.

Step 1: Configuring Trino to Connect to Amazon S3

To enable Trino to read from S3, we’ll set up an S3-compatible Hive catalog that allows querying data in S3 as if it were a database. This configuration will create a bridge between Trino and the cloud storage layer, giving us access to S3-stored files.

1.1 Setting Up Hive Catalog for S3

In the etc/catalog directory, create a new file called hive.properties with the following details:

• aws-access-key and aws-secret-key: Your AWS credentials for accessing S3.
• s3.endpoint: The endpoint for S3; for most users, this will be https://s3.amazonaws.com.
• metastore.catalog.dir: This points to the S3 path that will store the Hive metastore data. Replace your_bucket_name with the name of your S3 bucket.

Tip: Use environment variables or AWS IAM roles for secure access instead of hardcoding credentials.

Step 2: Creating and Querying an S3 Dataset

2.1 Preparing Data in S3

To follow along, prepare a sample dataset in S3. Let’s assume we’re using a CSV file named sales_data.csv stored in the S3 bucket, with a schema including order_id, customer_id, amount, order_date, and region.

Upload this file to your S3 bucket under a directory called sales.

2.2 Creating an External Table in Trino

With the data in place, create an external table in Trino that points to the S3-stored CSV. Use the following SQL query in the Trino CLI:

This command registers the CSV in S3 as an external table in Trino. Now, you can query sales_data just as if it were stored in a traditional database.

2.3 Running Queries on the S3 Data

With the sales_data table available in Trino, you can now run complex analytics queries directly on the data in S3. For example, to analyze total sales by region, use:

Since Trino queries the data directly in S3, there’s no need to move data locally, making it highly efficient for large-scale datasets.

Use Cases for Cloud Data Warehousing with Trino

By connecting to S3, Trino functions as a data lake query engine, enabling several powerful use cases:

1. Scalable Data Analytics: Analyze large datasets without data duplication or complex ETL pipelines.
2. Unified Data Access: Trino allows querying of S3 data alongside local databases, providing a unified view for analytics.
3. Cost Efficiency: Storing large datasets in S3 and querying them with Trino can be more cost-effective than maintaining a traditional data warehouse.

Best Practices for Using Trino with Cloud Storage

1. Partitioning: Organize data in S3 by date, region, or another category to improve query performance.
2. Columnar Formats: Consider converting CSV data to columnar formats like Parquet or ORC for more efficient storage and faster queries.
3. Caching: Use Trino’s caching feature to store frequently accessed data locally, reducing latency and S3 access costs.

Conclusion and Next Steps

Integrating Trino with cloud storage like Amazon S3 unlocks a new level of scalability and flexibility for data warehousing. With this setup, you can query and analyze large, diverse datasets in a cost-efficient manner, making Trino a powerful tool for modern data architectures. In the next blog, we’ll explore caching strategies and further optimize query performance for frequently accessed datasets.

Stay tuned as we continue to enhance our Trino project, building a robust analytics solution that meets the demands of big data.