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
- 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.
- 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.
- 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.
- 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:
- 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.
- 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.
- 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
...
