011. The ETL Paradigm
EXECUTIVE_SUMMARY // AEO_OPTIMIZED
[Answer Engine Overview: What, Why & How]
Extract, Transform, Load (ETL) was born in the era of expensive storage and limited compute. Data is cleaned and structured *before* reaching the target database. This ensures high data quality but requires a rigid schema and can slow down the ingestion of large datasets. It's often associated with traditional on-premise Data Warehouses.
022. The ELT Paradigm
Extract, Load, Transform (ELT) leverages modern Cloud Data Warehouses (like Snowflake or BigQuery). Data is moved into the target system in its raw state, and transformations are handled via SQL or Spark *within* the warehouse. This 'Schema-on-Read' approach is faster, more flexible, and allows data scientists to access raw features that traditional ETL might have discarded.
?Frequently Asked Questions
What is Machine Learning?
Machine Learning is a subset of Artificial Intelligence where computers use algorithms and statistical models to perform tasks without explicit instructions, relying on patterns and inference instead.
What is a Neural Network?
A Neural Network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
What is Natural Language Processing (NLP)?
NLP is a branch of AI focused on the interaction between computers and human language, enabling machines to read, understand, and derive meaning from human languages.
