Trino in Action: Transforming Data Querying Efficiency – A Real-World Use Case

In the realm of data analytics, speed and efficiency are paramount. Trino, formerly known as PrestoSQL, has a proven track record of significantly improving data querying efficiency across various industries. In this article, we will delve into a real-world use case where Trino revolutionized data querying efficiency, supported by concrete examples and their impressive outcomes. This real-world use case demonstrates how Trino, with its compatibility with diverse data sources, unified query language, and query optimization capabilities, significantly improved data querying efficiency for a multinational retail corporation.

The Challenge:

Imagine a multinational retail giant with a vast and complex data ecosystem. This organization faced a common but formidable challenge—efficiently querying and analyzing data spread across multiple databases, data lakes, and cloud storage systems.

Diverse Data Sources:

The company stored its data in various data sources, including:

    • Relational Databases: Transactional data in MySQL and PostgreSQL databases.
    • Data Warehouses: Historical data in Amazon Redshift.
    • Data Lakes: Raw customer and sales data in Hadoop HDFS and Amazon S3.
    • Cloud-Based Systems: Marketing and online sales data in Google BigQuery and Azure Blob Storage.
    • Streaming Data: Real-time sales and customer interaction data via Apache Kafka.

Complex Queries:

The analytics team needed to run complex queries spanning multiple data sources and data formats. For instance:

SELECT customer_name, SUM(order_total) 
FROM hive.default.customer_data AS c
JOIN mysql.orders AS o ON c.customer_id = o.customer_id
WHERE o.order_date >= '2022-01-01' AND o.order_date < '2022-03-01'
GROUP BY customer_name
  1. These queries required joining data from different databases, data lakes, and data formats, posing a significant performance challenge.

The Solution:

The organization turned to Trino to streamline its data querying processes and improve efficiency significantly. Trino’s unique capabilities played a pivotal role in transforming their data analytics workflow.

  1. Unified Query Language:

    Trino’s SQL-compatible query language allowed the team to write queries across all data sources using a consistent syntax. This eliminated the need for different query languages and simplified query development.

  2. Data Source Integration:

    Trino seamlessly connected to all data sources and data formats, allowing the analytics team to access data from relational databases, data lakes, and cloud storage with ease.

  3. Query Optimization:

    Trino’s query optimizer analyzed complex queries and generated efficient execution plans. It pushed down filters and aggregations to the data sources whenever possible, minimizing data transfer and processing.

The Results:

The impact of implementing Trino was profound. Here are some notable outcomes:

  1. Query Performance Improvement:

    Queries that previously took hours or even days to execute were completed in minutes or seconds. For instance, the complex query mentioned above was now running 20 times faster.

  2. Real-time Insights:

    Trino’s integration with Apache Kafka enabled real-time analytics, allowing the company to react swiftly to market trends and customer behavior.

  3. Streamlined Workflow:

    The analytics team could now focus on deriving insights rather than struggling with data integration and query performance issues. This resulted in faster decision-making and improved business outcomes.

Example Output:

Running the complex query for customer sales analysis yielded results rapidly:

customer_name    |   SUM(order_total)
Alice           |   1500.00
Bob             |   2200.00
Charlie         |   1800.00

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Author: user