Rust is a unique language whose traits make it very appealing for data engineering. In this session, we'll walk through the different aspects of the language that make it such a good fit for big data processing including: how it improves performance and how it provides greater safety guarantees and compatibility with a wide range of existing tools that make it well positioned to become a major building block for the future of analytics.

We will also take a hands-on look through real code examples at a few emerging technologies built on top of Rust that utilize these capabilities, and learn how to apply them to our modern lakehouse architecture.

Talk by: Oz Katz

Here’s more to explore: Why the Data Lakehouse Is Your next Data Warehouse: https://dbricks.co/3Pt5unq Lakehouse Fundamentals Training: https://dbricks.co/44ancQs

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Network protocols for transferring data generally have one of two problems: they’re slow for large data transfers but have simple APIs (e.g. JDBC) or they’re fast for large data transfers but have complex APIs specific to the system. Apache Arrow Flight addresses the former by providing high performance data transfers and half of the latter by having a standard API independent of systems. However, while the Arrow Flight API is performant and an open standard, it can be more complex to use than simpler APIs like JDBC.

Arrow Flight SQL rounds out the solution, providing both great performance and a simple universal API.

In this talk, we’ll show the performance benefits of Arrow Flight, the client difference between interacting with Arrow Flight and Arrow Flight SQL, and an overview of a JDBC driver built on Arrow Flight SQL, enabling clients to take advantage of this increased performance with zero application changes.

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Learn how Rust, the Apache Arrow project, and the Data Fusion Query Engine are increasingly being used to accelerate the creation of modern data stacks.

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This talk will introduce Polars a blazingly fast DataFrame library written in Rust on top of Apache Arrow. Its a DataFrame library that brings exploratory data analysis closer to the lessons learned in database research.

CPU's today's come with many cores and with their superscalar designs and SIMD registers allow for even more parallelism. Polars is written from the ground up to fully utilize the CPU's of this generation.

Besides blazingly fast algorithms, cache efficient memory layout and multi-threading, it consist of a lazy query engine, allowing Polars to do several optimizations that may improve query time and memory usage.

Read more:

https://github.com/pola-rs/polars https://www.ritchievink.com/blog/2021/02/28/i-wrote-one-of-the-fastest-dataframe-libraries/

Join the talk to learn more.

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