ELT Processes Using PySpark Cheatsheet (7 Pages)

Description

📗 𝐄𝐋𝐓 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 𝐮𝐬𝐢𝐧𝐠 𝐏𝐲𝐒𝐩𝐚𝐫𝐤

ELT(Extract, Load, Transform) using PySpark involves leveraging PySpark, which is the Python API for Apache Spark, to perform big data processing tasks.

𝐈’𝐥𝐥 𝐨𝐮𝐭𝐥𝐢𝐧𝐞 𝐚 𝐛𝐚𝐬𝐢𝐜 𝐄𝐋𝐓 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 𝐮𝐬𝐢𝐧𝐠 𝐏𝐲𝐒𝐩𝐚𝐫𝐤::

☑ 𝐄𝐱𝐭𝐫𝐚𝐜𝐭:
In this stage, you retrieve data from various sources such as databases, CSV files, JSON files, etc.
PySpark provides built-in functions and libraries to extract data from a wide range of sources.

☑ 𝐋𝐨𝐚𝐝:
Once the data is extracted, it needs to be loaded into an Apache Spark DataFrame.
You can create DataFrames from various sources using PySpark’s DataFrame API.
This step involves defining the schema of the DataFrame and loading the data into it.

☑ 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦:
After the data is loaded into DataFrames, you can perform transformations on it as per your requirements.
This may include filtering data, aggregating data, joining multiple DataFrames, applying user-defined functions (UDFs), etc.
PySpark provides a rich set of functions and APIs for performing these transformations efficiently in a distributed manner.