Learning about Apache Arrow in Pandas

Apache Arrow is an in-memory columnar data format that helps to efficiently store data between clustered Java Virtual Machines (JVMs) and Python processes. This is highly beneficial for data scientists working with Pandas and NumPy in Databricks. Apache Arrow does not produce different results in terms of the data. It is helpful when we are converting Spark DataFrames to Pandas DataFrames, and vice versa. Let's try to better understand the utility of Apache Arrow with an analogy.

Let's say you were traveling to Europe before the establishment of the European Union (EU). To visit 10 countries in 7 days, you would have has to spend some time at every border for passport control, and money would have always been lost due to currency exchange. Similarly, without using Apache Arrow, inefficiencies exist due to serialization and deserialization processes wasting memory and CPU resources (such as converting a Spark DataFrame to a Pandas DataFrame...