// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
ETL
ETL is a process where data is taken from various sources, cleaned and reshaped into a consistent format, and then moved into a target system like a data warehouse. It's like preparing ingredients (extract), cooking them (transform), and then serving the meal (load).
TECHNICAL DEFINITION
ETL is a data integration process involving extracting raw data from diverse source systems, transforming it to conform to business rules and target schema requirements (e.g., cleaning, aggregating, joining), and loading the refined data into a data warehouse or data lake for analytical processing and reporting.
BACKGROUND
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence (AI), its subdisciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, Glossary of machine vision, and Glossary of logic.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Data Integration
- Data Pipeline
- Data Warehousing Process
- Data Transformation
USAGE NOTE
ETL is commonly used for building traditional data warehouses for business intelligence.
DEVELOPERS
Organizations developing technology related to ETL.
Provides a unified data and AI platform, including extensive ETL capabilities through its Lakehouse architecture, essential for preparing, transforming, and managing data for AI models and LLM development.
Offers a cloud data platform that enables robust ETL processes using SQL and Snowpark, crucial for preparing and integrating data for AI/ML workloads, including feature engineering and prompt context data.
Through services like AWS Glue and Amazon Sagemaker Data Wrangler, AWS provides extensive ETL tools specifically designed for data integration, transformation, and preparation for machine learning models and AI applications.
Leverages services such as Google Dataflow for serverless ETL pipelines and Vertex AI for MLOps, enabling comprehensive data preparation and transformation essential for AI engineering and model training.
Offers Azure Data Factory for cloud-scale ETL and Azure Machine Learning for MLOps, providing integrated tools for data ingestion, transformation, and orchestration for AI initiatives.
Specializes in operationalizing features for machine learning, providing a feature platform that incorporates sophisticated ETL processes to transform raw data into production-ready features for real-time AI applications.
Provides automated data connectors (ELT) that simplify the process of extracting and loading data from various sources into data warehouses and lakes, which then feed AI engineering pipelines for model training and prompt context.
Focuses on programmatic data labeling and weak supervision, which involves sophisticated data transformation (T in ETL) to generate high-quality training data for AI models and improve prompt engineering.
Offers a platform for retrieval augmented generation (RAG) which involves ingesting, indexing, and preparing knowledge base data (an ETL-like process) to provide relevant context for LLMs and improve prompt responses.