Rust Vs. Scala Vs. Python For Data Engineering!

Summary

Highlights

• Python's dynamic typing and easy syntax make it a go-to for data engineers starting out. 📜
• Scala's tight integration with Spark makes it ideal for high-performance data applications. 💥
• Rust's memory safety features make it suited for high-performance and low-latency data engineering. 🔒

Key Takeaways

• Python is perfect for ease of development with its rich ecosystem, but it struggles with performance limitations. 🐍
• Scala excels in big data settings thanks to JVM and Spark optimization, but it has a steep learning curve. 💾
• Rust offers top-notch performance and memory safety for real-time processing, but lacks ecosystem support compared to Python and Scala. 🦀

Overview

Chapters

• 00:00 - 00:30: Introduction to Data Engineering Languages In the chapter titled 'Introduction to Data Engineering Languages', the speaker, referred to as 'data guy', introduces a video that aims to compare and contrast three prevalent languages used in the data engineering market for writing data pipelines. While noting that there are many tools like SQL within this space, the focus is on the three main tools currently dominating the field.
• 00:31 - 04:00: Overview of Python for Data Engineering The chapter begins with an overview of popular programming languages used in data engineering: Scala, Rust, and Python. Scala is highlighted as a statically typed language operating on the JVM, Rust is noted as a systems programming language, and Python is described as a dynamically typed interpreted language. The chapter aims to delve into each of these languages in detail to help beginners in data engineering identify the most suitable language for their work and needs.
• 04:01 - 07:00: Overview of Scala for Data Engineering The chapter 'Overview of Scala for Data Engineering' introduces the topic of selecting programming languages based on personal and project needs. It begins by discussing the aspects of different programming languages, specifically Python, Scala, and Rust. The focus is on understanding which language is best suited for particular tasks and use cases in data engineering. The chapter aims to help readers make informed decisions on which language to learn and use by considering factors like application, efficiency, and personal preference.
• 07:01 - 10:00: Overview of Rust for Data Engineering This chapter delves into the significance of the Rust programming language in the realm of data engineering. It is acknowledged as a highly popular language owing to its simplicity, readability, and comprehensive ecosystem. Rust excels particularly in domains such as ETL workflows, data analysis, machine learning, and workflow orchestration. A key feature is its dynamic typing and interpreted execution model, which facilitates straightforward development and debugging with minimal formal training. Consequently, Rust often emerges as the preferred language for data engineers starting out, bolstered by its extensive array of data processing libraries.
• 10:01 - 11:30: Conclusion and Language Recommendations The chapter covers the importance of Python in the realm of data transformation and manipulation using libraries such as pandas, dask, polars, and Spark, which leverage advanced compute engines. It emphasizes Python's role in workflow orchestration via tools like Apache Airflow, Prefect, and Dagster, all of which use Python as their primary language for defining data pipelines. Additionally, Python's seamless integration with machine learning and AI frameworks, including TensorFlow, PyTorch, and Scikit-learn, positions it as a crucial language for data science and AI-driven pipelines.

Rust Vs. Scala Vs. Python For Data Engineering! Transcription

• 00:00 - 00:30 hey y'all data guy here and today I have a video for you where I'm going to go through and kind of compare and contrast three of the most common languages I'm seeing within the data engineering marketplace right now um and where I you know just really want to hear them compared and contrasted a lot um so you know while tools like SQL and you know there's a lot of different tools within data in your Marketplace there's three main kind of tools i' would say that are being used for writing data pipelines
• 00:30 - 01:00 these days um and those are Scala rust and python um so Scala is a statically typed language running out of jvm rust is a systems programming language and python as you imagine is dynamically typed interpreted language um and what I want to do is talk about each of these in depth with a goal towards actually giving you an idea of hey if you're just starting to you know out in your data engineering Journey what language is the right one for you and the type of work
• 01:00 - 01:30 you want to be doing and also what language works for you kind of just and how it's written um and how it's used that's what I want to explore today so go through python Scala and rust talk about which each is best used for how they work what use cases they're best at um so you can decide which one you want to use and which one you want to spend your very limited time on this Earth learning and exploring so without further Ado let's get into it so the first language I want to talk about is python um so python is a really widely
• 01:30 - 02:00 probably the most widely used language in data engineering due to its Simplicity readability and extensive ecosystem um it's really dominant in ETL workflows data analysis machine learning and workflow orchestration um because it's Dynamic typing and interpreted execution model make it really easy to develop and debug without a lot of formal training and it's often just the first choice and the first language data engine will learn um but one of its greatest strengths is in its Rich ecosystem of data processing Library
• 02:00 - 02:30 pandas dask polars P spark enable you to do efficient data transformation manipulation leveraging more advanced compute engines um while aache airflow prefect Daxter all powerful workflow orchestration tools that all use python as their major language for defining data pipelines um python also seamlessly integrates with machine learning and AI Frameworks like tensorflow pytorch psyit learn so it's also crucial language for data science and AI driven pipelines um
• 02:30 - 03:00 because most Ai workflows and Frameworks are dependent on python as their kind of main use language for interaction um however despite its use ease of use Python does have some pretty significant performance limitations um because it's interpreted and dynamically typed it's much slower than compiled languages like rust or jbm based languages like Scala um and python also suffers from Global interpreter lock so it really limits its ability you it's complex term but really
• 03:00 - 03:30 what it limits its ability to do is true parallel execution in multi-threaded environments um CPU is or python is really inefficient for CPU bound Tas because it can't make use of really true parallel execution but multi processing having many Python scripts running independently can be used as a workaround um and then there's another drawback is Python's memory management which relies on garbage collection and reference counting um and this is you know it's constantly looking back making sure everything is referenced properly
• 03:30 - 04:00 um and also collecting all the garbage and extra systems that are produced by the actual processes um and this can introduce performance drawbacks in bottlenecks when you're doing large scale data processing workloads so it's much less suitable for real time or load latency applications um and really I would say to summarize kind of python is best suited for data engineering workflows that involve ETL data analysis ml pipelines worklow orchestration um and it's really best in IO bound
• 04:00 - 04:30 workloads where there's less limitations in terms of cpub bound processing you know not massive data sets U but it's ideal for ad hoc data exploration prototyping scripting um and building the logic around you know how you're managing more complex processes and maybe other systems that can handle those uh you know parallelism and and more heavyweight data processing easier now the next language I want to talk about is Scala um and Scala is a statically typed Java virtual machine based language that blend L functional
• 04:30 - 05:00 and objectoriented programming paradigms it is one of the most popular languages for big data processing because it's got a really deep integration with Apache spark um and since spark was originally written in Scala its apis and internals are optimized for it making it a probably the best choice for writing high performance spark applications um and one of scale's most significant advantages is performance and scalability it runs on the Java virtual machine and benefits from just in time compilation which optimizes execution
• 05:00 - 05:30 over time um additionally Scala provides excellent concurrency support through ACA streams Futures parallel collections all just scale of features for running parallel jobs which makes it really well suited for handling large scale distributed data processing workloads it also offers a strong type safety so reducing runtime errors in large data processing applications um but one of its drawbacks is a much steeper learning curve um people don't love Scala out there um if if you're not super familiar
• 05:30 - 06:00 with functional programming it Sy syntax can be really complex and compilation speed can be slow you're kind of trading off hey compile it now and then have it run fast versus compile it fast um at runtime and then have kind of the overall speed be a little bit slower um especially compared to dynamically typed languages like python um but despite these drawbacks it's a really good choice for big data processing um especially things like spark Flink kofka streams where you want to get in the nitty-gritty and really optimize those workloads so it makes it really ideal
• 06:00 - 06:30 for both batch and streaming workloads um and then also its interoperability with Java allows easy integration to existing Enterprise ecosystems so if you're already pretty Java Centric at your company Scala is probably a good language to explore because it really is I would say one of the best programming language that interacting with Java in the virtual machine and other Java applications um so highly recommend it for those kind of use cases um but if you're just doing Simple lightweight things that you would typically use
• 06:30 - 07:00 Python for Scala might be a bit Overkill um and you know also you're just getting into Data engineering probably don't want to start with Scala um because it's going to be really difficult to pick up and learn um and there's not a ton of resources out there for learning it like there are for uh languages like python um so just something to keep in mind as well now the last language I want to talk about is one of the newest ones on the scene for data engineering um and that is rust rust is a systems programming language that's really rapidly getting popularity in the data
• 07:00 - 07:30 engineering space due to its high performance um it's also got really good memory safety and a strong concurrency model um unlike Scala and python actually rust is a compiled language that generates machine code so that's bike code zeros and ones um that you directly feed into the machine um and that eliminates the performance overhead typically associated with interpreted or jvm based languages um and one of Russ standout features is its memory safety without garbage collection um it's a to
• 07:30 - 08:00 achieve this through Innovative ownership and borrowing system which prevents memory leaks and ensures data Integrity in multi-threaded applications um and this makes it really well suited for real-time data processing and ETL jobs that require both low latency and high efficiency where it's able to have you know kind of the flexibility of python but without the garbage collection um that is typically associated with it it also helps provide high efficiency highly efficient concurrency so it's really designed for
• 08:00 - 08:30 concurrent parallel workloads U by preventing data races at compile time this makes it a really excellent choice for high throughput multi-threaded data processing workloads um and unlike python which struggles with concurrency due to that Global interpreter lock interpreter lock I mentioned earlier rust allows safe and efficient parallel execution however rust does have some limitations when it comes to ecosystem support and data engineering while you have libraries like Apache Arrow like data Fusion like polars that have support for
• 08:30 - 09:00 rust rust still does lack the extensive data processing ecosystem that Python and Scala offer um additionally rust learning curve is much steeper uh and compilation times are also going to be much longer due to strict safety checks um and ensuring a lot of those great features it comes great power comes great safety shacks and great compilation times um and so Russ is really best suited for low latency ETL processing High concurrency High parilis um things like real time streaming analytics embedded data processing on
• 09:00 - 09:30 constrained Hardware um and it's also ideal for developing high performance data processing libraries because you're able to by you know interpreting into machine code you're able to squeeze every bit of performance out of your machines um and so that's why you've seen you know the adoption of rust based tools like polars and patchy arrow for efficient column or data handling where that stuff really matters because you're working at really large scale um data so now that we've gone through all three languages I just want to end it with kind of a quick summary um on when you should use language so I would say
• 09:30 - 10:00 use Python when you know you're just starting out you're trying to write ETL workflows data transformation pipelines orchestrating external workflows using you know things like airflow um integrating with machine learning models AI applications or performing you know like things like ad hoc data analysis because you're able to do that really quickly with python um then I would say you know once you're looking at distributed big data processing um you know you need to use things like spark to actually get these workloads done efficiently that's when you might want to look at Scala um also for streaming
• 10:00 - 10:30 applications like kofka Flink AA stream Scala is really well partnered with those because it's Java based um and also if you are working in a jvm based environment scale is already J jvm compatible so really useful for those situations as well um and then also for handling large scale batch processing jobs too rust you're going to want to use when you're developing low latency high performance ETL jobs um you know processing data in real time streaming analytics systems and also working with memory efficient column or data formats
• 10:30 - 11:00 like arrow and paret um and then you're also uh you know if you need to implement con current and parallel data processing applications with really strict safety requirements rust is a good idea for that because it's built in at the core um so anyways that's the quick video I wanted to make if there's other language you want me to explore that you think uh weren't mentioned here let me know we'll make a future video but Above All Else hope you can learn something hope you have a great rest of your day day guy out