All about Unstructured Data Extraction
Welcome to the ultimate guide on unstructured data extraction! This guide is designed to help you quickly grasp unstructured data extraction, equipping you with the tools and techniques to unlock valuable insights from raw, unstructured PDFs, CSVs, websites and more.
Written by Jason Llama · May 2025
Learn about
Data
Understanding unstructured data and how to work with it effectively.
Pipelines
Building powerful pipelines to extract and structure data from text, images, video, audio, and more.
Generative AI
Applying advanced techniques like NLP, OCR, large language models (LLMs) and generative AI to automate processes.
Scaling
Scaling your data extraction capabilities for real-time applications in industries like marketplaces, eCommerce, and finance.
This resource provides everything you need to become proficient in unstructured data extraction, whether you're a beginner or an experienced developer.
With a focus on practical examples, this tutorial explores how modern technologies like AI, large language models and vision systems have revolutionized the way we process unstructured data, positioning you to stay ahead in the ChatGPT era.
Quick start
Unstructured data extraction in 5 minutes — learn how to take a PDF, put it in front of gpt-4o, and extract useful information in 3 simple steps.
What is unstructured data extraction?
Overview of this whole guide
Text, documents, images, video, audio and logs
How LLMs unlocked unstructured data extraction
Explains the roles of NLP, OCR and large vision models
How unstructured data extraction drives your AI strategy
Explains why extraction is foundational to any AI roadmap
Identifying positive, negative and neutral sentiment
Identifying people, places, organizations and more
Relation extraction
Uncovering connections between entities
Event extraction
Detecting and structuring occurrences from raw inputs
Evaluating AI - correctness, consistency, safety, security, quality
The many dimensions of evaluating AI
ROI of unstructured data extraction
Measuring time savings, accuracy gains, real-time insights and scale benefits
Unstructured data extraction in different industries
Marketplaces
Extracting listings, reviews and user signals.
eCommerce
Parsing product catalogs, feedback and imagery.
Finance
Structuring reports, news, transcripts and market logs.
Defense
Analyzing imagery, signals intelligence and field reports.
Getting started with unstructured data extraction
Getting unstructured data
Ingesting via email, web scraping and cloud integrations.
Storing unstructured data
Choosing storage: S3, Elasticsearch or vector databases (Weaviate, Pinecone, Qdrant).
Building an AI pipeline locally
Preprocessing unstructured data
Cleaning noise, scraping, tokenization and language detection.
Extracting structure from unstructured data
HTML, PDF, form and spreadsheet parsing plus LLM-powered extraction and metadata capture.
Useful simple enrichments
Sentiment tagging, categorization, schema and knowledge-graph mapping.
Validating extracted data
Techniques for accuracy checks and schema enforcement.
Presenting your data
Summarization, deduplication, entity linking and normalization.
Testing your local pipeline
Strategies for unit tests, sample runs and debugging.
Deploying an AI pipeline in the cloud
EC2 deployment
Running your pipeline on virtual machines.
Serverless deployment
Scaling extraction with functions-as-a-service.
AWS Glue
Managed ETL for unstructured data pipelines.
Deploying an AI pipeline on a platform
Trigger.dev
Low-code workflows for event-driven extraction.
Prefect
Robust orchestration with Python-native flows.
n8n
Open-source, node-based automation for data ingestion.
Airflow (hosted)
Managed scheduling and dependencies.
Real time extraction with RAG
Database + LLM = RAG
Search basics and how LLMs made it better