Introduction to DataFrames
Explore how to create and manage DataFrames in Databricks with PySpark, understanding their structure, immutability, and lazy evaluation to handle distributed data efficiently.
We'll cover the following...
Why data must be distributed
When the data is small, it can fit on one computer. You can open it, read it, and process it easily. However, real-world data is often large, growing, and shared by many users.
Databricks is built for this kind of data. Instead of loading everything into one machine, Databricks processes data across many machines at the same time. This idea is called distributed data.
Distributed data means the data is split into parts and stored or processed in multiple places instead of one. You do not need to know where each part lives. Databricks and Spark handle that for you. From your point of view, the data still looks like one table, and you work with it normally. This is why beginners can use Databricks without worrying about servers, machines, or networking.
DataFrames are the structured interface that lets you work safely and easily with that distributed data as though it were one simple table.
Even small datasets in Databricks are treated as distributed data, so your code works the same way when data grows later. ...