Data processing

Data processing, in the context of web scraping, refers to storing, handling, managing, and analyzing the data that is generated from scraping. In previous chapters of the book, we focused on the concept of effective and efficient scraping with code examples.

As the demand for data is growing, technologies are also evolving and adapting to new changes. Currently, as there has been a boom in AI/ML-based systems, there is competition to provide easy and quick solutions to problems without compromising on quality.

In the coming sections, we will introduce some technologies that help with data processing.

PySpark

The Python library for Apache Spark, pyspark (https://spark.apache.org/), is used to process and analyze data, especially of a large volume. Spark is a framework that is used to handle big data (data with variety, volume, and velocity) and is more effective than Hadoop (https://hadoop.apache.org/), a framework for parallel processing, scheduling, and...