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Three Reasons Why a Data Scientist is Twice as Efficient with PrestoDB

There’s an increased need for data analysts, scientists, and engineers to do ad hoc analytics with vast volumes of data. Data is becoming the centerpiece for large organizations, and it’s important to query multiple sources accurately and with great efficiency. 

Data experts spend a lot of their time on data preparation and collection. It’s a big issue that affects how their typical workday looks, but this is not just a problem for data scientists on a personal level. 

The issue affects the whole business because the lack of access to data means that data scientists will need more time to generate relevant data. Simply put, data scientists need to work on data and get valuable conclusions out of it, not spend hours organizing columns of data. 

The Solutions to Large Volumes of Data 

Data scientists commonly use coding languages such as Python and R to do their jobs. Why? Because they are perfect for running complex mathematical algorithms. Most scientists aren’t interested in learning SQL since it’s not suitable for their needs, and it isn’t required as a skill set. 

However, data scientists have to get quick access to data often stored in multiple places like data lakes, data warehouses, Hadoop, etc. That’s why they need to use SQL query engines to request data where it resides and save time they would spend on acquiring data.

Presto is a distributed SQL query engine that allows data scientists to do all of these things. Presto lets analysts use their preferred programming software while letting Presto run in the background for quick data movement. 

What is Presto and How it Works 

As we mentioned earlier, Presto is a distributed SQL query engine. Facebook created it because it needed to run constant interactive queries on large volumes of data within Hadoop clusters. Since 2013 Presto is completely open-source software, and anyone can use it. 

Presto can work with relational databases such as MS SQL, PostgreSQL, or MySQL. However, it can also work with non-relational data such as Hadoop, Amazon S3, and HDFS. The real beauty of Presto compared to much other similar software is that it can query data regardless of where it’s located. 

At the same time, there are options for managed Presto and serverless approaches. That’s why Presto is becoming such a popular choice for engineers and data scientists. Now let’s see what makes Presto so efficient compared to other similar solutions. 

1. Separated Storage & Computation  

On average, a data analyst can save around two hours each day by using Presto. That’s because it’s designed to run fast analytic queries for data clusters of all sizes and structures. It integrates seamlessly with data ecosystems, and there’s no need to do any modifications. 

It gives a whole new computing layer that allows faster analytics. It doesn’t store data, allowing it to scale queries up or down quickly based on the current needs. In other words, Presto works on data abstraction to allow such effective scaling and perform analytics in an economical way. 

The high-performance query design of Presto makes ETLs obsolete. This separation of computation and storage of data is what makes Presto much faster and efficient. However, it also gives organizations the ability to use resources independently and more cost-efficiently. 

2. Better Performance Than Competition 

Presto was initially developed because Facebook couldn’t use Apache Hive for interactive queries. This software performed well for complex and larger projects, but it wasn’t meant to provide low latency queries. Other solutions like Apache Spark also use in-memory computation, but that works only for larger projects and doesn’t offer the same efficiency as Presto. 

The core architecture of Presto is designed for high performance and features like pipeline execution, in-memory processing, and code generation all improve performance. There’s no creation of additional JVM containers with Presto queries – it relies on a long-lasting  JVM process that avoids any overhead. 

Benchmarking has revealed that Presto performs 2 to 7.5x times faster than Hive, and that’s a difference no one can ignore. 

3. More Options and Better Insights 

Presto allows data scientists to stay within their preferred domain (programming language), get access to a variety of data, and get a couple of hours more each day. Professionals can use this extra time to do critical work and develop creative ways to gain insights from different sources. 

Creative and critical thinking is essential for a data analyst. More time means having the option to assess different scenarios, make assumptions, and try alternative approaches. On top of that, the ability to access and add multiple data sources lets you discover new data mining and investigation opportunities. 

That’s simply impossible to do with traditional approaches, especially now when efficiency is essential for all companies. 

Conclusion 

Presto offers many advantages for interactive queries and data analysis. It’s not a surprise that Presto is the software choice for many large organizations like Facebook, Netflix, Airbnb, Dropbox, and so on. This abstracted data architecture is the future when it comes to data querying. 

Let’s not forget that this is open-source software that you can adjust according to your organization’s needs. The data analysts can adjust Presto exactly how they see fit to make their workflows as efficient as possible.

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