What’s the Difference Between Trino and PrestoDB?
Some say that the age of the 4th industrial revolution has already begun, and advanced digital technologies are the main driving factors.
The truth is that they help interconnect physical, digital, and biological worlds, resulting in the development of the latest, most advanced technologies. These include AI, machine learning, natural language processing, quantum computing, robotics, 3D printing, and more.
All these technologies depend on one crucial element – big data. The more advanced technologies become, the more data they generate, thus increasing the need to analyze such immense amounts of information.
However, that’s precisely where the main challenge is – finding the solution capable of querying and analyzing trillions of gigabytes of raw, unprocessed data efficiently, timely, and cost-effectively. That’s how the need for distributed cloud query solutions was developed.
Solutions like Trino and PrestoDB help companies analyze vast amounts of data with utmost precision and in a convenient way. Let’s see what these solutions are and how they differ.
Reason Why the Open-Source Project Has Split
Created at Facebook in 2012, PrestoDB was designed to help expedite 300 PB Data Warehouse queries due to them being time-consuming. Companies needed a solution capable of connecting to several databases or data warehouses while providing a simple user interface and a range of integrations for BI tools.
Presto was designed as the solution that would encompass all this and more. It’s a solution capable of quickly accessing, querying, and analyzing incredible amounts of large-scale data with improved efficiency. As time passed, PrestoDB grew into PrestoSQL – a solution with similar if not better capabilities and properties, more suitable for companies and different situations.
As the need to gather and analyze more and more data grew, businesses realized that they needed something even more powerful than PrestoSQL. In 2020, PrestoSQL made its transformation into Trino – a solution more capable than its predecessor that was designed with big data business needs in mind.
This transformation into Trino makes it a perfect solution for cloud computing stacks. Trino can interface directly with a range of separate data sources as its connector design supports such actions. More importantly, Trino is a fantastic data processing solution as it can work with pools and lakes of raw data stored in cloud storage solutions, including AWS S3 and HDFS data blocks.
In addition, Trino is also an excellent solution for handling various relational databases such as MySQL and Microsoft SQL. Trino can be upgraded to create a visual interface that allows businesses to improve performance and create real-time databases, making it perfect for companies that need to analyze massive volumes of data.
Trino helps them get the job done while saving time, effort, and resources along the way.
Pros and Cons of Trino and PrestoDB
Let’s see how Trino differs from PrestoDB and what makes it a better solution for handling your big data needs.
Trino Provides Exceptional Levels of Versatility and Convenience
While originally designed for Facebook, Trino is much more than just that. It has become a fully comprehensive SQL querying engine. Thanks to its immensely powerful MPP architecture, Trino is highly scalable and flexible, making it perfect for many industries that rely on big data.
However, the biggest strength of Trino lies in its core – the querying engine that has separate computing and storage. Trino uses separate connectors to scour data from other data sources. Because of that, it provides the highest level of versatility when it comes to querying traditional databases, non-relational databases, columnar databases, and other data sources.
Trino also excels at allowing users to run ad hoc queries with SQL regardless of the location of data. It completely eliminates the need to ETL the data to another system. Instead, Trino uses the existing data storage to provide access to data for analysis.
Advanced Reporting and Dashboard
Trino allows users to create personalized reports and unified dashboards to better query multiple data sources. It can be quite valuable for businesses that need to query multiple data sources independently, thus eliminating the need to hire data platform teams.
While Trino is an excellent solution for companies that run ETL queries against multiple data sources, it also provides options to save resources and gather more output – a perk that makes Trino better than Presto.
Even though Trino is more advanced than Presto, PrestoDB has a few advantages compared to Trino as it provides certain features that aren’t found in Trino.
Those features include:
- Project Aria – perfect for processing file formats like ORC.
- Project Presto Unlimited – a memory-saving feature for creating temporary in-memory buckets.
- Additional user-defined functions such as dynamic SQL functions support.
- Presto-on-Spark – a library within Spark executor.
Trino vs PrestoDB: Which One Is Better?
Both PrestoDB and Trino are excellent solutions. When it comes to which one is better, there’s no one-size-fits-all answer. Trino supports platforms with high data generation, like Facebook, and is a perfect solution for companies that have to deal with immense amounts of data daily.
Some of the largest companies today use Trino, including Facebook, Amazon, and Netflix. It’s not that one solution is better than the other – it’s just that Trino is simply a more modern and advanced version of what PrestoDB used to be.
What the Future Holds
While Trino may be in the spotlight at the moment, Presto hasn’t changed. Trino is now more than just an engine as it provides so many features and options for businesses to handle their significant data needs.
Trino is just continuing the same thing that Presto has been doing for a long time. Both solutions are still valid. Presto has been improving for quite some time now, and the same can be said for Trino, especially today when there is an ever-growing need for resource management, cost-efficiency, and data processing.