Polars vs Apache Spark from a Developer's Perspective

#Polars and #Spark 3 are both popular frameworks for processing large datasets. But which one is better for you? Let's see how its laid out.

Firstly, Polars is built with Rust(built with) and can use Python as a interface, while Spark 3 supports Java, Scala(built with), and Python and even JavaScript. This means that if you're already working with one of these programming languages, you might find it easier to integrate with Spark 3. Rust is a relatively new kid on the block and its awesome! What about Go you say? Well, we will discuss that and Apache Beam some other time.

For the #data world and when on-boarding data scientists, I have found it useful to leverage their knowledge of python to speed up the process. Both Polars and Spark support python as a common factor.

When it comes to architecture, Polars is more lightweight and easier to use, while Spark 3 has more built-in optimization techniques and can handle more complex workloads like machine learning and graph processing.

In terms of performance, Polars is faster than Spark 3 for some data manipulation tasks, especially those that can be parallelized. This is because Polars takes advantage of modern processors to use SIMD instructions, which can give you a big speedup. Spark 3 doesn't have native support for SIMD processing.

If you're working with really large datasets, Spark 3 might be the better choice for you because it can run on a cluster of multiple nodes, giving you more processing power. Polars is better suited for smaller datasets and deployment on a single node or a few nodes.

Last but not the least, consider the codebase required to work with each framework. Polars has a smaller, more streamlined codebase that's easier to use and maintain, while Spark 3 has a larger, more complex codebase with more functionality. Add to this, how well trained your team is to handle Rust or Python and how much time you may have as a #leader.

One more twist in the proverbial tale, not a lot of people have heard of Polars. So selling it would be harder in the corporate world. It will eventually be a well known one for sure. There are some amazing #benchmarks out there that says how good it is.

The choice between Polars and Spark 3 comes down to what you need. If you're working with smaller datasets and want something that's easier to use, Polars might be the way to go. But if you need more processing power, scalability, and more advanced functionality, Spark 3 might be the better choice.

One more approach, off the topic is #duckdb which has been making some great strides lately visit https://duckdblabs.com/. We will talk a out this some other time.

My two bits: Stick to Pandas with Arrow for small amount of data(duh!). Polars if it is medium. For scale out go to spark. Important #duckdb #polars are best suited for scale up not scale out.

#polyglot #spark spark #polars (ritchievink) https://www.pola.rs/ #dataengineering #datanalytics #arrow #apache #python #pandas #rust