Data & automation terms, explained.
Short, plain-language definitions for the terms that come up when scoping a web scraping, data pipeline, AI, or automation project — each linked to the service that implements it.
Web scraping
Automated extraction of data from websites — turning listings, prices, or public records that only exist as web pages into structured, usable data (JSON, CSV, or a database).
ETL (Extract, Transform, Load)
A pipeline that pulls data from a source (an API, a database, a scraper), cleans and reshapes it, then loads it into a destination like a data warehouse or operational database — usually on a schedule.
Reverse ETL
The opposite direction of a normal ETL flow: syncing clean, modeled data from a warehouse back into operational tools (CRM, ad platforms, support tools) so business teams can act on it.
RAG (Retrieval-Augmented Generation)
An AI pattern where a language model retrieves relevant chunks of your own documents or data before answering a question, so responses are grounded in your actual content instead of the model's general training.
OCR (Optical Character Recognition)
Technology that converts scanned documents, PDFs, or images of text into machine-readable text — the first step before extracting structured data from contracts, invoices, or court filings.
Anti-bot detection
Systems websites use to identify and block automated traffic — browser fingerprinting, rate limiting, CAPTCHAs, and behavioral analysis. Production scraping has to be engineered around these defenses.
MLS data
Property listing data from a Multiple Listing Service — the databases real estate agents use to share listings. Accessing it programmatically at scale usually requires scraping public listing sites rather than the closed MLS system itself.
Churn scoring
A machine learning model that ranks customers by how likely they are to cancel or stop buying, based on behavioral signals — so teams can prioritize retention efforts where they matter most.
Data pipeline
The general term for any automated system that moves data from one place to another, applying transformations along the way — scrapers, ETL jobs, and streaming systems are all types of data pipelines.
Vector database
A database optimized for storing and searching embeddings — numerical representations of text or data used to power semantic search and RAG systems, finding results by meaning rather than exact keyword match.