Unifying Data Sources: Trino’s Compatibility and Data Format Mastery

Trino, formerly PrestoSQL, is renowned for its ability to unify and query data from a wide range of sources and diverse data formats. In this comprehensive article, we will explore the types of data sources compatible with Trino and how it effectively manages various data formats, supported by real-world examples. Trino’s compatibility with various data sources and its adeptness at managing diverse data formats make it a powerful tool for data analytics and querying. Whether your data is stored in relational databases, data lakes, message brokers, or cloud warehouses, and whether it’s structured, semi-structured, or columnar, Trino provides a unified platform to seamlessly access, analyze, and derive insights from your data. Its versatility and extensibility make it a valuable asset for organizations dealing with heterogeneous data landscapes.

Data Source Compatibility:

Trino is compatible with a plethora of data sources, including:

Relational Databases: Trino can connect to popular databases like MySQL, PostgreSQL, Oracle, and SQL Server. For example:

SELECT * FROM mysql.sample_data WHERE column_name = 'value'

Trino seamlessly queries data from MySQL.

NoSQL Databases: Trino can interact with NoSQL databases like Cassandra and MongoDB, allowing SQL-like queries over non-relational data.

Data Lakes: Trino supports querying data lakes, including Hadoop Distributed File System (HDFS) and cloud-based storage like Amazon S3 and Google Cloud Storage (GCS). For instance:

SELECT * FROM hdfs.default.sample_data WHERE column_name = 'value'

Trino can access and analyze data stored in HDFS.

Message Brokers: Trino connects to message brokers like Apache Kafka, enabling real-time analytics over streaming data.

Cloud Data Warehouses: Trino can query data in cloud data warehouses such as Amazon Redshift and Google BigQuery.

Data Format Flexibility:

Trino’s flexibility extends to data formats, including but not limited to:

Parquet: Trino can efficiently query Parquet files, a popular columnar storage format. For example:

SELECT name, age FROM parquet.default.employee_data WHERE department = 'Engineering'

ORC: Trino supports querying ORC (Optimized Row Columnar) files, another columnar storage format commonly used in the Hadoop ecosystem.

JSON and XML: Trino can parse and query JSON and XML data, making it versatile for semi-structured data analysis.

Example Output:

Suppose you have a data lake with Parquet files containing employee data. You run the following query:

SELECT department, AVG(salary) 
FROM hdfs.default.employee_data 
WHERE hire_date >= '2022-01-01' 
GROUP BY department

Trino’s compatibility with HDFS and Parquet files allows you to effortlessly obtain results:

department      |   AVG(salary)
Engineering     |   80000.00
Sales           |   75000.00
Marketing       |   72000.00

Custom Connectors:

Trino’s extensible architecture enables the creation of custom connectors to access proprietary or specialized data sources. This makes it adaptable to unique data integration needs.

Schema Evolution:

Trino can handle schema evolution, allowing queries to adapt to changes in the data source schema over time without disruptions.

Author: user