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Trino vs Snowflake

In the world of data analytics, data warehouses have always been important. But it seems that they’ve become even more valuable for the whole process in the past couple of years. This became evident because more and more solutions are coming out constantly—not many of them showing signs of poor reception in the current market. We will be exploring the differences between Trino vs Snowflake.

On top of that, data scientists are always trying out something new and looking for better options. Developers are also looking for different approaches to improving these platforms. All of this results in better performance and more flexibility

But sometimes, it can be difficult for data engineers and analysts to choose the right solutions. You have to dig deep in order to establish if a particular solution is adequate for your goals or not. This is why we’ve decided to help and create a Trino vs Snowflake comparison, two very popular options. 

About Trino

Trino is an SQL engine that was created by building on the Presto project. In the beginning, it was called PrestoSQL, but the company decided to rebrand it in 2020 and call it Trino. The goal behind its development was always to focus on running fast queries regardless of the data type or volume. 

Trino has a connector architecture capable of working with a variety of data sources. Most importantly, it can work with both non-relational and relational data sources. Trino was designed to run ad hoc queries on big data platforms like AWS S3, Google Cloud, Hadoop HDFS, and so on. 

Presto was basically created for the needs of Facebook. However, as Facebook’s needs changed and the project grew, it ultimately became Trino in 2020. They still use the great foundation set by Presto to build for the future, which means that even though their brand is new, they still have a track record of a veteran solution.

About Snowflake

Snowflake is your typical data warehouse that can be built using Microsoft Azure Cloud or Amazon Web Services. It doesn’t require any specific software or hardware to be selected, installed, set up, or managed. This is one of the reasons why Snowflake is very popular among companies that rely on big data. 

Its design enables it to support companies with a need for scalable and flexible big data solutions. At the same time, it allows them to host business intelligence solutions which is another plus.

Snowflake was created in 2012 and launched in 2014 after the company determined that it could, in fact, help a lot of other companies. This is what we love about both of these solutions—both were tested internally for a time before being released to the public. 

For many, Snowflake has kept the data warehousing industry alive. The company created a different platform that had new things to offer. That’s what made it such a popular choice. 

How they work

 

Trino

 

Even though it works in this domain, Trino isn’t a data warehouse, and it doesn’t have a storage system users can rely on. As we mentioned earlier, it’s an exclusively distributed SQL query. It allows companies to perform data querying with all kinds of different data volumes, structures, and sources. 

It works the same way, even if a business has multiple storage options. Trino was built with two main goals in mind: low latency and efficiency. It lets companies perform queries quickly without having to invest in new processing capabilities or IT hardware. 

Trino also separates compute and storage while querying data is stored by third-party systems. It’s one of the best data abstraction solutions today. Apart from Facebook that developed the solution originally, Trino is also used by LinkedIn, Airbnb, Netflix, and so on—quite a few heavy hitters. 

Snowflake

 

Snowflake is an essential part of every data ecosystem. Snowflake is built for the analytics aspect of data, but it doesn’t offer a lot when it comes to machine learning, data science, or application integration. 

Users have to choose which of the public clouds they want to use with Snowflake, AWS, or Azure. It’s designed to help companies work with heavy data science workloads. It offers a lot of flexibility as it separates compute and storage processes. 

Users pay storage for each terabyte they used during that month. On the other hand, computation is billed per second used. 

Key benefits of Trino

 

Reduced complexity

 

Given the fact that Trino computes and stores data separately, it brings a lot of benefits. First of all, data engineers take care of the management and all the complexities that this creates behind the scenes. 

The users doing queries are entirely unbothered by the number of data sources, where they might be, and which backing technologies run in the background along with the changes that happen to them. 

Easy extension

 

Apart from its own connectors, Trino allows users to build new ones with ease. It has a plugin that makes this process simple while making sure there are no shading and classpath issues. Its Apache License makes it easy to extend without any legal issues, as well. 

Easy scaling

 

Trino scales easily with both massive data processes and smaller applications. The fact that Trino is an engine and not a storage platform makes it versatile and scalable. Since compute and storage are separate, it means that companies can scale them independently according to their needs. 

No vendor lock-in

 

Since Trino is an open-source engine, there is no risk of vendor lock-in. Trino gives you more freedom when you’ve reached the limits of your current storage and compute power. It lets you make all the important decisions regarding deployment and node configuration. 

Key benefits of Snowflake

 

Great support and storage option

 

Snowflake lets users combine semistructured and structured data for their data analytics efforts. This data can be loaded into a cloud database without having to transform it into a different schema. The queries and storage are optimized automatically by Snowflake. 

Good performance

 

Cloud has a flexible nature, meaning that you can scale the virtual warehouse when there’s a need for a large number of queries or when the data needs to be loaded faster. When this need is no longer present, users can scale down and only pay for the added resources they used. 

Accessibility and consistency

 

Traditional data warehouses often have concurrency issues when there are a large number of uses within a short timeframe. 

Lots of queries are using up resources, and this causes failures and delays. The multicluster architecture of Snowflake helps with these problems as all virtual warehouses work separately and can be scaled independently. 

Trino or Snowflake?

 

Even though both of these solutions separate the compute and storage and can be used for similar applications, they have different approaches. 

Snowflake lets users move all of their data within one database. This offers certain advantages and improves performance compared to traditional data warehouses. 

However, moving all of the company’s data in a single place is often impossible and includes a variety of manual tasks. 

Trino doesn’t care where the data is stored, which saves a lot of time and money. Even though Trino is a query engine, it has the potential to take over the sphere of commercial data warehouses, bringing an entirely new approach. On top of that, it offers all of the benefits of OSS and managed services, which are the core of the commercial benefits of vendor software – a win-win situation. It is clear which data management warehouse is the best between Trino vs Snowflake.

Conclusion

 

We hope this post has helped you understand the differences between Trino vs Snowflake. Naturally, you need to take a good look at your process, infrastructure, and your needs before you can determine which one to use.

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