Introduction to Big Data workflows

  • In ML projects, it is often necessary to process large amounts of data, known as Big Data. This can pose a significant challenge for traditional data processing tools and techniques. Fortunately, there are a variety of Big Data processing engines available today that are designed to handle these challenges, such as Apache Spark and Polars.

Spark is a powerful Big Data processing engine that allows you to work with large datasets in ML projects. It provides a flexible and scalable platform for data processing, allowing you to process data in real-time or in batch mode, and enabling you to run complex algorithms and models on massive datasets.

Polars is another Big Data processing engine that is gaining popularity due to its speed, scalability, and ease of use. It is an open-source data processing library designed for processing large, complex datasets with ease. It provides a Pandas-like API for data manipulation, enabling users to easily perform a wide range of data processing tasks, such as filtering, aggregating, and transforming data.

Both Spark and Polars offer powerful solutions for handling Big Data in ML projects, and choosing the right engine largely depends on the specific needs and requirements of your project. Regardless of which engine you choose, however, both Spark and Polars have revolutionized the way that we process and analyze large datasets, and have enabled us to unlock valuable insights and knowledge from data that was previously impossible to handle.

Notebooks

Polars

Spark