ETL vs. ELT: The Data Pipeline Showdown!
Data pipelines are the workhorses of data engineering, moving data from source to analysis. This post explores ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) approaches, along with data cleansing and transformation techniques.
ETL: Transform data before loading (like prepping ingredients before cooking).
ELT: Load data first, then transform within the target system (like throwing everything in the pot and then cleaning/chopping).
The right approach depends on factors like data size and processing needs.
Data pipelines also involve data cleansing and transformation:
Data Cleansing: Fixing errors, inconsistencies, and missing values in raw data.
Data Transformation: Preparing data for analysis through techniques like aggregation, joining tables, and deriving new features.
Python libraries like pandas and PySpark can be used for data cleansing and transformation.
This is your gateway to the world of data engineering pipelines.
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Extract Transform load (ETL) and Extract Load and Transform (ELT)